Semiotic Analysis of Clinical Chemistry: for “Knowledge Work” in the Medical Sciences

Semiotic analysis of clinical chemistry: For “knowledge work” in the medical sciences

By

Helen F. Carberry BAppSc (Med Tech) (QIT), GD Nutr & Diet (QIT), GD Media (AFTRS).

Being a dissertation submitted in fulfilment of the requirements for the degree of Doctor of Philosophy, Centre for Innovation in Education, Faculty of Education, Queensland University of Technology.

2003

QUEENSLAND UNIVERSITY OF TECHNOLOGY

DOCTOR OF PHILOSOPHY THESIS EXAMINATION

CANDIDATE NAME: Helen Frances Carberry

CENTRE: The Centre for Innovation in Education

PRINCIPAL SUPERVISOR: Dr James Watters

ASSOCIATE Dr Clare O'Farrell SUPERVISOR(S): Dr Terry Walsh

THESIS TITLE: Semiotic analysis of clinical chemistry: For “knowledge work” in the medical sciences

Under the requirements of PhD regulation 16.8, the above candidate presented a Final Seminar that was open to the public. A Faculty Panel of three academics attended and reported on the readiness of the thesis for external examination. The members of the panel recommended that the thesis be forwarded to the appointed Committee for examination.

Name: Dr James Watters Signature: ……………………………… Panel Chairperson (Principal Supervisor)

Name: Dr Carmel Diezmann Signature: ……………………………… Panel Member

Name: Dr Gordon Tait Signature: ……………………………… Panel Member

Under the requirements of PhD regulations, Section 16, it is hereby certified that the thesis of the above-named candidate has been examined. I recommend on behalf of the Examination Committee that the thesis be accepted in fulfilment of the conditions for the award of the degree of Doctor of Philosophy.

Name: Professor Tom Cooper Signature: ………………………… Date: ……… Chair of Examiners (Head of School or nominee) (Examination Committee)

Keywords

Medical science, clinical chemistry, Mode 1 disciplinary, Mode 2 transdisciplinary knowledge systems, knowledge work, symbolic analysis, culture, discourse, cultural analysis, competence, literacy, semiotics, semiology, structure, logic, rhetoric, ideology, signs, representations, inscriptions, contexts, systems.

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Abstract In this thesis a socio-cultural perspective of medical science education is adopted to argue the position that undergraduate medical scientists must be enculturated into the profession as knowledge workers and symbolic analysts who can interact with computers in complex analytical procedures, quality assurance and quality management. The cue for this position is taken from the transformations taking place in the pathology industry due to advances in automation, robotics and informatics. The rise of Evidence-Based Laboratory Medicine (EBLM) is also noted and the observation by higher education researchers, that knowledge systems are transforming in such a way that disciplines can no longer act in isolation. They must now collaborate with disparate fields in transdisciplinary knowledge systems such as EBLM, for which new skills must be cultivated in undergraduate medical scientists. This thesis aims to describe a theoretical basis for knowledge work by taking a semiotic perspective. This is because, semiotics, a theory of signs and representations, can be applied to the structure of transdisciplinary scientific knowledge, the logic of scientific practice and the rhetoric of scientific communications. For this purpose, a semiotic framework is first derived from a wide range of semiotic theories existent in the literature. Then the application of this semiotic framework to clinical chemistry knowledge, context, logic, and rhetoric is demonstrated. This is achieved by interpreting various clinical chemistry data sources, for example, course materials, laboratory spatial arrangements, instruments, printouts, and students’ practical reports, collected from a teaching laboratory situation. The results of semiotic analysis indicate that the clinical chemist working in the computerised laboratory environment performs knowledge work, and the term is synonymous with symbolic analysis. It is shown that knowledge work entails the application of a systematic structure for clinical chemistry knowledge derived in terms of the validation procedures applied to laboratory, data, results and tests; the application of logic in the classification and selection of instruments, their rule- governed-use, and in troubleshooting errors; pragmatic decisions based on availability of space, services and budgets; discrimination among values in laboratory test evaluations in EBLM, for the cost-effectiveness and relevance of pathology services; and the recognition of rhetorical strategies used to communicate

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laboratory test information in graphs, charts, and statistics. The role of the laboratory context is also explained through semiotics, in terms of its spatial arrangements and designs of laboratory instruments, as a place that constrains the knowledge work experience. This contextual analysis provides insights into the oppositional trend brought to wide attention by analysts of computerised professional work, that more skills are needed, but that there are fewer highly skilled positions available. The curriculum implications of these findings are considered in terms of the need to cultivate knowledge workers for highly complex symbolic analysis in computerised laboratories; and also the need to prepare medical science graduates for the transdisciplinary knowledge system of EBLM, and related avenues of employment such as biomedical research and clinical medicine. In meeting the aims to define and demonstrate knowledge work from the semiotic perspective, this thesis makes an original contribution to knowledge by the application of semiotics to a field in which it has probably never been tested. It contributes to the scholarship of teaching in higher education by formulating a structure for transdisciplinary medical science knowledge, which integrates scientific with other forms of knowledge, and with real world practice.

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Table of Contents

Certificate of acceptance ii Keywords iii Abstract iv Contents vi Appendices ix List of figures x Glossary xii Abbreviations xv Graphic symbols xvi Statement of original authorship xvii Acknowledgments xviii

Chapter 1: “Knowledge work” in the medical sciences

1.1 Introduction 1 1.2 Thesis background and context 2 1.3 The rationale and significance of this investigation 3 1.4 Aims, research questions and objectives 11 1.5 Design methodology 13 1.6 Thesis layout 15 1.7 Conclusion 16

Chapter 2: Medical science industry, work and profession

2.1 Introduction 18 2.2 The medical science profession 18 2.3 Transformations in the pathology industry 23 2.4 Knowledge work and symbolic analysis in clinical chemistry 28 2.5 Conclusion 36

Chapter 3: Medical science in the context of higher education

3.1 Introduction 37 3.2 Systems of knowledge production in higher education 38 3.3 Kinds of university for liberal and vocational education 45 3.4 Educational reforms and strategies 50 3.4.1 Competence and capability 51 3.4.2 The challenge of work-based learning 54 3.4.3 Competency-based standards for medical scientists 57 3.5 Discourse competence, knowledge work and symbolic analysis 61 3.5.1 “D” competence: Operational, cultural and critical literacy 64 3.5.2 A framework for knowledge work and symbolic analysis 69 3.6 Conclusion 75

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Chapter 4: A semiotic framework for Discourse and “knowledge work”

4.1 Introduction 77 4.2 Structuralism and semiology 79 4.2.1 The internal structure of language 82 4.2.2 Internal and external linguistics 89 4.2.3 Semiology and systems of objects 98 4.2.3.1 Semiological levels of analysis 99 4.2.3.2 Objective analysis: The denoted system 103 4.2.3.3 The connotations of objects 108 4.2.4 Material semiotics and applications 110 4.2.4.1 A semiological approach to the food system 112 4.2.4.2 The connotations of household objects 115 4.2.4.3 Semiology, material semiotics and education 117 4.2.5 Socio-semiotics applied to clinical chemistry Discourse 120 4.3 Logic and pragmatics in scientific practice 121 4.3.1 A semiotic model of Discourse and culture 123 4.3.2 The sign as triad 128 4.3.3 Sign classification and modes of inference 133 4.3.4 Typology of Discourse in terms of sign systems 138 4.3.5 A syntactic, semantic and pragmatic model of Discourse 140 4.3.5.1 The terminology applied to cultural units and semantic fields 141 4.3.5.2 Cultural units structured in semantic fragments 142 4.3.5.3 Transportation as cultural phenomenon 146 4.4 Conclusion 150

Chapter 5: Design methodology for semiotic analysis

5.1 Introduction 152 5.2 Research strategies used in cultural analysis 153 5.2.1 Paradigms, traditions and approaches 154 5.2.2 Data collection techniques for semiotic analysis 157 5.3 Cultural analysis applied to clinical chemistry 160 5.3.1 Research location and participants 161 5.3.2 Data sources and data collection techniques 163 5.3.3 Data analysis 165 5.3.3.1 The structure of clinical chemistry Discourse 166 5.3.3.2 The logic of clinical chemistry laboratory practice 167 5.3.3.3 The rhetoric of laboratory testing 169 5.4 Reliability and validity issues addressed 170 5.5 Conclusion 171

Chapter 6: The structure of Clinical Chemistry Discourse

6.1 Introduction 172 6.2 A structure of clinical chemistry Discourse 173 6.2.1 The “substance of the content”: Western medicine 174 6.2.2 A structure for clinical chemistry laboratory practice 178 6.2.2.1 Clinical chemistry transdisciplinary Mode 2 knowledge 178 6.2.2.2 Validation in clinical chemistry laboratory practice 180

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6.3 The laboratory as a system of objects 185 6.3.1 Spatial analysis of the teaching laboratory 185 6.3.2 Alternative industry laboratory spaces 192 6.3.3 Implications of spatial analysis 196 6.3.4 The “connotations” of laboratory instruments 198 6.3.4.1 The “rhetoric” of laboratory instrument design 200 6.3.4.2 The ideology of laboratory testing 207 6.4 Conclusion 213

Chapter 7: Logic in clinical chemistry laboratory practice

7.1 Introduction 215 7.2 Classification of chemical analysis systems 216 7.2.1 Spectrophotometry 220 7.2.2 MAS in the plane of expression 223 7.2.2.1 The MAS expression line 224 7.2.2.2 The expression side and instrument selection 230 7.3 Logic in instrument use and troubleshooting errors 236 7.3.1 Deduction in the ideal use of instruments 237 7.3.2 Deduction in data handling and interpretation 240 7.3.3 Troubleshooting: Error detection and diagnosis 243 7.3.3.1 Error detection and diagnosis in instrument use 245 7.3.3.2 Error detection and diagnosis in data handling 250 7.3.3.3 Error detection and diagnosis in quality monitoring 255 7.3.3.4 The consequences of laboratory error 259 7.4 Knowledge work and symbolic analysis in clinical chemistry 261 7.5 Conclusion 263

Chapter 8: The rhetoric of laboratory testing

8.1 Introduction 265 8.2 The range of competencies used in clinical chemistry Discourse 266 8.3 Pragmatics in laboratory testing 268 8.4 Rhetoric in laboratory testing 270 8.5 Conclusion 278

Chapter 9: “Knowledge work” in the medical sciences

9.1 Recapitulation of research problem 279 9.2 Summary of findings of semiotic analysis 281 9.3 Contribution to knowledge and higher education 288 9.4 Limitations, reliability and validity issues 290 9.5 Further research 292 9.6 Recommendations 295 9.7 Conclusion 296

References: 298

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Appendices

A. Australasian Association of Clinical Biochemists (AACB) Syllabus 323 B. Undergraduate Clinical Biochemistry Course Materials 329 B1. Clinical Biochemistry Unit Outlines 329 B2. Glucose Practical Protocol 332 B3. Glucose Master Report 333 B4. Experiment Stages 338 B5. Student Glucose Practical Report 339 B6. Barbiturate Practical Protocol 344 B7. Barbiturate Master Report 345 C. Instrument printouts 347 C1. Serum Spectral Scans 347 C2. Barbiturate Scan – Expected 348 C3. Barbiturate Scan - Sample unwashed 349 C4. Barbiturate Scan - Extraction loss 350 D. Glucose Practical Report Data Summary 351

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List of figures

4.1 A history of structuralism and semiotics 78 4.2 The linguistic sign as dyad 84 4.3 Sign model of language 85 4.4 Cultural sign model 90 4.5 Linguistic structure 94 4.6 Connotative semiotics 97 4.7 Levels of analysis 101 4.8 The plane of expression 104 4.9 Food as cultural system 113 4.10a The sign as triad 130 4.10b Interpretation 130 4.10c Unlimited semiosis 131 4.11 Classification of signs and modes of inference 134 4.12 Modes of inference 135 4.13 The dimensions of semiotics 139 4.14 Semantic fragment 142 4.15 Ideological connotations 146 4.16 Cultural sign model of transportation 147 4.17 Semantic fragment of transportation 148 4.18 Levels of analysis of scientific Discourse 150

6.1 A cultural sign model of Health 177 6.2 The laboratory test loop 181 6.3 Validation in clinical chemistry 183 6.4a Teaching laboratory 186 6.4b Instrument laboratory layout 188 6.5a Core industry laboratory 192 6.5b Automated instruments 192 6.5c Core industry laboratory layout 193 6.6 Total laboratory automation 194 6.7a Glucometer 195 6.7b POCT by ISE 195

7.1 Chemical analysis systems 219 7.2a EMR cultural sign model 220 7.2b EMR theory net/content 222 7.3 Pharmacia Biotech Ultraspec UV/Visible spectrophotometer 225 7.4 The class of spectrophotometers 226 7.5 MAS in the plane of expression 231 7.6 MAS semantic fragment 233 7.7 The Beer-Lambert Law 239 7.8 Rule based MAS use 240 7.9 MAS calculations 241 7.10a Least Squares Sum (LSS) 242 7.10b LSS assumptions 242 7.11 Troubleshooting errors in MAS use 247 7.12 Plasma colours and spectra 247

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7.13 Barbiturate experiment 248 7.14a Pathlength error 249 7.14b Light path error 249 7.15 Compliance with Beer’s Law 252 7.16 Troubleshooting errors in data handling 253 7.17 Graphical interpretation of data 253 7.18 QC data and statistical summaries 257 7.19 Levey-Jennings Charts 258

8.1 Levels of competence in clinical chemistry 267 8.2 Expanded MAS semantic fragment 269 8.3 Prediction of tumour ‘Z’ and benign condition ‘Y’ using serum ‘X’ 273 8.4 Dot plot of frequency distribution for serum ‘X’ 274 8.5 ROC curve 275 8.6 Connotations of laboratory test cut-off values 277

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Glossary

Abduction: hypothetical reasoning by guesswork, probable inference or conjecture, from cues such as symptoms, and clues in the environment leading an observer to infer facts from what cannot be directly observed, by consulting or by inventing a rule (result → rule → case).

Architectonic: the internal structure of scientific knowledge, division, classification and system, based on internally coherent rules, laws and principles, derived and verified in a sequence of observation, hypothesis, experiment, and conclusion, description, explanation, and prediction.

Commutation: the substitution of an element in the expression line or syntagm of a sign system (system of expression-content relations) which brings about a correlative change in the content.

Deduction: formal logic by which there is a necessary connection between premises and conclusions, conclusions follow logically from premises, deductive inferences are conclusive by necessity (rule → case → result).

Expression line: the existence of the component elements of an object or stretch of language co-existing in relations of combination or contiguity in linear series (see also syntagm).

Expression side: the axis of association called forth by significant elements that have potential existence by virtue of their associations with distinctive, similar but different elements or invariants in the expression line (see also paradigm).

Form of the content: the conceptual structure (interrelated theories) applied to the substantive content of a knowledge system; also applies to ideology, values or world visions that influence the way phenomena are seen.

Form of the expression: the structure of objects, or morphological characteristics, for example words in language.

Ideology: a belief system, language system, knowledge system, or practice, to the exclusion of others, sometimes deliberately in the interests of power and control.

Induction: probable inference of a general rule based on observations of particular cases (result → case → rule).

Invariant: a significant minimal unit, element or component in an expression line which is significantly different to alternative components and hence a point from which signification emerges.

Logic: chain of reasoning, meta-thinking or thinking about thinking, inference, the drawing of conclusions about things seen and unseen, from evidence based on certain premises.

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Mode 1 knowledge system: knowledge system that produces specific disciplinary knowledge, drawing on rules, laws, principles and procedures, relatively autonomous and free from external influences, associated particularly with the sciences.

Mode 2 knowledge system: transdisciplinary knowledge system, in which knowledge is acquired by collaborations between disciplinary experts from widely differing fields come together transiently to solve specific problems on specific occasions at specific sites of applications, and demonstrating economic and social accountability.

Paradigmatic relations: relations between similar but different components of a sign system co-existing in a vertical axis of meaning by virtue of associations based on similarity and difference; identified at significant points (invariants) in an expression line and from which an expression side emerges.

Plane of content: the interrelations between the contents of knowledge systems (content-content, signified-signified relations), in a semantic system, for example, the interrelations between theories in the physical sciences.

Plane of expression: combined analysis in the expression line or horizontal axis or linear series (division); and the associations called forth at points of invariance constituting a paradigmatic axis or expression side, from which classifications are made.

Pragmatics: modification of rule-governed procedures based on theoretical considerations by taking into account the context and circumstances in which theories apply; also representing relations between signs and interpreters

Purport: an unanalysed phenomenon waiting to be ordered by specific disciplines in their own specific ways, the matter under consideration, being a continuum comprising, potentially, all possible ways of forming a particular phenomenon.

Rhetoric: the art of persuasion conducted by the manipulation of representations, not what is represented, written or spoken, but the way it is represented, written or spoken.

Semantics: the relations between signs and their culturally coded meanings, given in signified-signified relations, analysed in the plane of content.

Sign: anything that stands in for something else – word, object, feeling, event, interpreted by culturally coded equivalence (≡) or an act of inference (⊃); a representation linked with a concept or idea in the mind in a specific way (representation-object-interpretation).

Signified: that to which the signifier refers; the cultural associations that arise from the representative aspect of a sign, referred to also as the content or reference.

Signifier: the representative aspect of a sign, specifically in linguistics, a sound image or phoneme, referred to as an expression in non-verbal language systems.

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Signifying matrix: an aspect of an object from which a signification emerges, comprising a non-signifying support, and an invariant feature which causes the signification to occur, because of relations of association with similar but different, alternative features.

Semiosis: thinking in signs, sign action, reasoning from sign to sign with some pragmatic purpose in mind.

Substance of the content: substantive disciplinary content of an unanalysed phenomenon awaiting formation in some way.

Substance of the expression: material substance given morphological characteristics in the form of expression once it is ordered in some way.

Syntactics: sign-sign relations and the structured relations between the representative aspects of signs and sign systems – signifier-signifier relations, analysed in the plane of expression.

Syntagmatic relations: relations of combination of elements of an analysed text or expression co-existing in linear series (expression line), and from which invariants are identified as significant points from which signification emerges.

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Abbreviations

AACB Australasian Association of Clinical Biochemists AIMS Australian Institute of Medical Scientists AAS Atomic Absorption Spectrophotometry CBS-MS Competency-Based Standards for Medical Scientists CHD Coronary Heart Disease CLIA88 Clinical Laboratory Improvements Amendments CPE Continuing Professional Education CV Coefficient of Variation CY Chromatography EBLM Evidence-Based Laboratory Medicine EBH Evidence-Based Health EBM Evidence-Based Medicine EMR Electromagnetic Radiation EMS Electromagnetic Spectrum FES Flame Emission Spectrophotometry HPLC High Pressure Liquid Chromatography HTA Health Technology Assessment ISE Ion-selective electrodes ISO International Organisation for Standardisation LPLC Low Pressure Liquid Chromatography MAACB Member Australasian Association of Clinical Biochemists MAS Molecular Absorption Spectrophotometry MFS Molecular Fluorescence Spectrophotometry MPLC Medium Pressure Liquid Chromatography NATA National Association of Testing Authorities NOOSR National Office of Overseas Skills Recognition NPT Near Patient Testing NMR Nuclear Magnetic Resonance OECD Organisation for Economic Cooperation and Development POCT Point-of-Care Testing POV Point of Variation or Invariance PSA Prostate Specific Antigen

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QA Quality Assurance QC Quality Control QM Total Quality Management QUT Queensland University of Technology RCT Random Controlled Trial RPL Recognition of Prior Learning SD Standard Deviation SV Sign Vehicle TLA Total Laboratory Automation WBL Work-Based Learning UV-VIS Ultra-Violet-Visible Light

Graphic symbols

≡ Relation of equivalence ⊃ Relation of implication ≠ Different from • Relation of simple combination /..../ Word as signifier or expression //....// Object as signifier, or expression <<....>> Signified/content/reference

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Statement of Original Authorship

The work in this thesis is my own work and has not been previously submitted for a degree or diploma at any other higher education institution. To the best of my knowledge and beliefs, the thesis contains no material previously published or written by another person except where due reference is made.

Signed: …………………………………

Date: ………………………………..

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Acknowledgments

This thesis has been an adventure into semiotics, a discipline that bridges the sciences and humanities, which is rarely if ever encountered in medical laboratory science. The realisation of this thesis would not have been possible without the input and assistance of various people, particularly thesis supervisors, work supervisors, and colleagues. I would like to thank the two sets of excellent supervisors for their commitment to high standards, and their respect for the student’s experience. On the first supervisory team, I would like to acknowledge Peter Hoeben from the Department of Biochemistry and Molecular Biology in the School of Life Sciences at QUT, for his encouragement and enthusiasm in the early stages of the thesis, and for challenging me to be “scientific”. I would like to acknowledge my first principal supervisor, Colin Symes who made this semiotic adventure possible, who challenged me to tackle difficult questions, to express difficult ideas in writing, and for being patient in times of extreme difficulty. On the second supervisory team, I would like to acknowledge Clare O’Farrell and Jim Watters for having the courage to take on the supervision of this thesis after Colin and Peter had departed to other regions. Clare made a big difference in helping me to clarify difficult structuralist issues and with matters of style in writing. Jim deserves a particular mention for taking on the principal supervision of this thesis in its most difficult stages, for helping me give structure to the thesis, for enthusiasm, encouragement, patience, tolerance and persistence in helping me bring this thesis to completion. Thanks also to my work supervisors in the School of Life Sciences, Terry Walsh and Adrian Herington, for supporting a crossdisciplinary thesis in the Department of Biochemistry and Molecular Biology; Ron Epping for providing me with laboratory photographs; and Cyril Craven for giving encouragement and access to clinical chemistry laboratory classes and course materials. I also acknowledge my colleagues in the pathology industry, particularly at Sullivan and Nicolaides Pathology laboratories for providing me with up to date information and laboratory photographs. Last but not least, thanks to my family and friends for being patient and putting up with me for the duration of this thesis.

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Chapter 1 For “knowledge work” in the medical sciences

1.1 Introduction

The purpose of this thesis is to apply semiotics, a powerful tool for analysing signs and representations, to the analysis of clinical chemistry professional knowledge and practice. The context of this analysis is undergraduate medical science education, for which clinical chemistry is a core discipline. The knowledge required for clinical chemistry practice has changed dramatically in recent years due to advances in medical science knowledge, automation, robotics and informatics. This thesis aims to reflect on these changes and give structure to the knowledge needed by graduating medical scientists for knowledge work in the “new knowledge” environment of clinical chemistry, for lifelong learners as well as employers. These reflections are based on the premise that clinical chemists add value in the workplace if they have a grasp of the structure of the knowledge they use, apply logic in their work practices, and evaluate and revise them as needed. They also add value by providing critical perspectives of laboratory tests, their clinical relevance, cost effectiveness and appropriateness, to meet the requirements of socially accountable laboratory medicine, in Evidence-Based Laboratory Medicine (EBLM). In order to reflect on clinical chemistry knowledge and practice in this way, medical laboratory science is understood as a scientific culture. As participants in a community of professional practitioners, clinical chemists share scientific languages and practices, and common goals, towards disease diagnosis and monitoring, regardless of how vital a role they believe pathology testing plays in health care in general. Semiotics is the method chosen to reflect on these issues, because it provides an integrative tool of cultural analysis, applicable to the structure of medical science knowledge, the logic of practice, and the rhetoric used in communicating laboratory test information to the recipients of pathology services. This chapter describes the context and significance of the research problem, poses the research questions, aims and objectives, and sets out how the research will be conducted.

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1.2 Thesis background and context

This thesis was motivated by a desire to understand why otherwise capable clinical chemistry students with a background in physics, chemistry and mathematics, failed to make connections between chemical analysis systems in the selection of instruments used to acquire data from experiments; failed to integrate theory and practice in the use of instruments; failed to detect, diagnose and troubleshoot errors; and failed to assess the quality of data and results represented in different formats in graphs, charts and statistics. Clinical chemistry is concerned with analysis of the chemical components of blood (and other body fluids) such as glucose and cholesterol, to assist with disease diagnosis, prognosis and monitoring (Australian Institute of Medical Scientists, [AIMS], n.d.). The data sources used to build a picture of professional knowledge and clinical chemistry laboratory practice are drawn from a student laboratory situation in an Australian university engaged in medical science education. These data include course materials, archival documents of practical activities, instrument printouts, and information from professional body websites, industry laboratories, textbooks, journals and newsletters. Differences between the medical science disciplines, clinical chemistry, haematology, immunology, microbiology, histology and cytology will be discussed briefly in Chapter 2 which describes work in the pathology industry. The rest of this thesis refers exclusively to clinical chemistry and medical scientists who specialise in clinical chemistry. Debates about who is qualified for the title clinical chemist - chemical pathologist, chief scientist or medical scientist, will be avoided because such lines of demarcation will not, in this case, help to structure clinical chemistry knowledge or clarify the meaning of professional laboratory practice. The structure of clinical chemistry knowledge in general provides the focus, not the particular levels that distinguish between undergraduate and postgraduate levels of clinical chemistry knowledge. Clinical chemists working in industry and universities share the same information in textbooks and journals, and belong to the same professional body, the Australasian Association of Clinical Biochemists (AACB), but they do not necessarily share perspectives on higher education. This thesis favours academic rather than industry perspectives because it is the function of medical science education to prepare students, using first principles, for work in the pathology 3 industry. The university is also unhampered by the demands for productivity and efficiency experienced in the pathology industry, which may mask the academic components of professional knowledge sought in this thesis. This imbalance will be addressed in recommendations in the final chapter. The structure of clinical chemistry knowledge and practice is considered in the light of changes taking place in the pathology industry, along with changes in universities. These changes provide the rationale for this thesis and are next considered along with approaches to the problems of so-called higher learning in universities.

1.3 The rationale and significance of this investigation

Three significant issues, changes in the pathology industry, changes in higher education and the production of knowledge, and matters of higher learning brought to light by research into higher education, have bearing on the way professional knowledge and practice in clinical chemistry might be conceived. The latter issue converges on semiotic theory which provides the unifying framework from which this thesis proceeds. The first consideration is the dramatic changes experienced by the pathology industry in the last twenty years due to rapid advances in automation, robotics, and informatics (AACB, 1999a, 2001; Rosenfeld, 1999; Wilding, 1998). As a result, clinical chemists perform far fewer manual activities, preparation of chemical reactions in test tubes, instrument calibrations, calculations, troubleshooting instrument malfunctions and routine maintenance. Different aspects of these activities are performed by service engineers, automation, robotics, and microprocessor algorithms. Most of the work now involves clinical interpretation of results and quality evaluations requiring the juggling of numbers and symbols in tables of data, graphs, charts and statistics, although computerised diagnostic Expert Systems that approximate human expert problem-solving capacities, perform many of these activities as well (Sikaris, 2001). Laboratory management which aims to improve productivity, efficiency and quality, is similarly supported by computerised quality management systems (QM) (AACB, 1998b; Elevitch, & Spackman, 1999; Weiss, & Ash, 1999; Westgard, & Klee, 1999). In Australia, part of the role of QM is to ensure compliance with the Medicare Reimbursement Schedule so that patients are eligible for Medicare rebates (Farrance, 2000). As part of this process, laboratories 4 are required to demonstrate in a more systematic way than has traditionally been the case, the clinical relevance, cost-effectiveness and appropriateness of laboratory tests, in EBLM (Morris, 2000; Price, & Hicks, 1999; Price, 2001). A question arises from this situation: Do medical science students need to cultivate troubleshooting and pragmatic managerial skills if these activities are being performed by Expert Systems and computerised quality management systems? The answer to this question is yes, particularly with respect to Expert Systems because they are still in their primitive stages and, being programmed as they are for expected error occurrences, Expert Systems do not pick up anomalous findings which require human expert intervention (Bedard, & Chi, 1992; Chi, Glaser, & Farr, 1988; Gillies, 1996; Jackson, 1999; Sikaris, 2001). It is important that students be encouraged to develop troubleshooting skills, and to develop at least a basic understanding of the concerns laboratory managers must constantly address. This prepares them for more advanced troubleshooting activities and team based interactions at work. Analysts of work have noted a shift in the nature of work in computerised technical environments, along the continuum from manual operations, manipulations of objects such as laboratory instruments, to mental work manipulating data and symbols in tables, graphs, charts and statistics (Aronowitz, & DiFazio, 1994; Barley, & Orr, 1997; Gibbons, Limoges, Nowotny, Schwartzman, Scott, & Trow, 1994). The terms knowledge work (Drucker, 1993) and symbolic analysis (Reich, 1992) coined to describe computerised intellectual labour, apply in the clinical chemistry laboratory, but they must be specified more clearly. Career options might also be considered because automation and computerisation are leading to fewer opportunities for highly skilled work in laboratories (AACB, 1999a; Aronowitz, & DiFazio, 1994; Wilding, 1995, 1998). The second significant issue to consider is a shift in the way knowledge is produced due to pressures of economic globalisation, advances in communication technologies, and the increasing complexity of real world problems, economic, genetic, environmental and social, generated by the production of scientific knowledge (Gibbons et al., 1994). As a result of this combination of factors, different disciplinary knowledge systems now collaborate on complex problems, and a rough distinction is drawn between two broad conceptions of knowledge production to characterise the situation. The term Mode 1 is adopted to refer to disciplinary, specialised, professional, scientific knowledge, codified in scientific laws and 5 principles and validated by internal peer-review (Gibbons et al., 1994, pp. 1-3). In the natural sciences, physics, chemistry, geology and biology provide examples of Mode 1 knowledge systems which are based mainly in universities and government research institutions. As Gibbons et al. argue, it is difficult for Mode 1 knowledge systems to solve complex problems in isolation, the disciplines need to collaborate with other fields and disciplines. The new mode of knowledge production, referred to as Mode 2, is transdisciplinary and subsumes Mode 1 disciplinary knowledge systems, placing them in wider economic and social contexts. Mode 2 transdisciplinary knowledge, in responding to the needs of science and society, is expected to be useful, economically viable, and socially accountable (pp. 9-15). The biotechnology industry, involving collaborations between scientific disciplines, big business, legal and political institutions, not necessarily based in universities, is an example of a Mode 2 knowledge system in the life sciences (p. 24). The medical laboratory sciences are traditionally associated with clinical diagnostics using the technological applications arising from science research. Transdisciplinary research activities are blurring the boundaries between pure and applied science, creating the potential for research activities by medical scientists, particularly in the domain of molecular diagnostics (AACB, 1999c). It is acknowledged that the terms Mode 1 and Mode 2 applied to knowledge systems lack precision, but they are used in this thesis to distinguish between disciplinary knowledge as produced by physics, chemistry and biology, and transdisciplinary knowledge as produced by collaborating groups in domains such as clinical medicine, biotechnology and EBLM. The emergence of transdisciplinary knowledge systems puts pressure on universities to demonstrate the relevance of their courses, and to find ways to expand the horizons of disciplines to encompass Mode 2 knowledge issues (Brennan, Fedrowitz, Huber, & Shak, 1999; Gibbons et al., 1994). Some higher education researchers (Boud, 1998; Boud, & Walker, 1998; Boud, & Solomon, 2001; Symes, & McIntyre, 2000) are exploring the possibilities of breaking down the dichotomy between workplaces and universities. They aim to disrupt the false distinction between vocational and liberal approaches to education, vocational that is, in the narrow sense of preparing people only for a specific work purpose, and liberal in the broad sense of cultivating general life skills that can be adapted to many and varied occupations for the benefit of communities, employers and lifelong learners (Candy, Crebert, & O’Leary, 1994; Gerber, & Lankshear, 2000; Symes, & McIntyre, 2000). 6

Contextual factors in learning are given recognition and also the learning that takes place at work. It is considered that workplaces can provide suitable subject matter for curricula and Bachelors Degrees (Boud, & Solomon, 2001; Symes, & McIntyre, 2000). The third significant issue for consideration is the changes taking place in higher education. In Australia, debates about the purpose and quality of higher education were particularly intense in the late 1980s and early 1990s, with the phasing out of the binary system of elite sandstone universities and vocational colleges of advanced education (Aulich, 1990). Industry representatives participating in the debates were critical of university graduates who failed to demonstrate critical thinking, and effective teamwork and communication skills (Business/Higher Education Round Table [B/HERT], 1992; National Board of Employment, Education and Training [NBEET], 1992). Higher education research has been since that time aimed at improving the generic and marketable skills of graduates (HEC, 1992; OECD, 1997). Other lines of inquiry focus on students’ learning and how students give structure to disciplinary (Mode 1) knowledge and interrelate it with other forms of knowledge in transdisciplinary (Mode 2) knowledge. The problem of the structure of knowledge is addressed by general studies into learning which are relevant in all avenues of higher education. They describe what students do with learning tasks, and roughly divide learning techniques into “surface, atomistic” and “deep, holistic” (Marton, Hounsell, & Entwistle, 1997). A surface, atomistic approach is demonstrated by students when they focus on discrete units of knowledge or facts, applying rote-learning techniques. Deep learning is demonstrated by students who solve problems and critically reflect on the material to be studied, grasping its overall structure and purpose. Problem-based Learning (PBL) is an example of a student-centred approach that aims to foster deep learning. In PBL students are presented with problems to be solved through independent study and peer-group collaboration (Boud, & Feletti, 1997). Other researchers (e.g. Biggs, 1999a, 1999b; Ramsden, 1992) have investigated teaching, claiming that surface learning is the student response to teaching techniques that fail to align teaching objectives with assessments, or assessments that fail to encourage deep learning techniques. Investigations into professional education as conducted by Schön (1983, 1987) are particularly relevant for medical science education. Although Schön’s 7 investigations were initially aimed at reflective practices in the architecture profession, they are applicable to many, perhaps all professions as wide ranging as engineering and occupational therapy (Schön, 1991). As Schön (1987) argues, although professional courses aim to emulate real-world problem solving activities of expert professionals, these practices are not well addressed in professional education programs that follow a research university model privileging systematic scientific knowledge (p. xi). The nature of human expertise has been dealt with specifically in the cognitive sciences in conjunction with studies into Artificial Intelligence (AI), given that humans provide the cognitive models and knowledge structures AI is designed to mimic (Bedard, & Chi, 1992; Chi et al., 1988; Gillies 1996; Jackson, 1999). In order to understand expertise in technical professions it is useful to consider the way Expert Systems are programmed, because they approximate human experts’ problem-solving capacities; and in turn consider how experts perform certain activities, structure their knowledge, make decisions, troubleshoot instrument malfunctions and diagnose illnesses (Bedard, & Chi, 1992, p. 135). There are criticisms of cognitive models because they focus only on mental processes. In other approaches to higher learning, it is argued that learning takes place when it is “situated” or embedded in the relevant context and culture applicable to the material being studied (Brown, Collins, & Duguid, 1989; Boud, & Walker, 1998; Jacob, 1992; Laurillard, 1993, 2002; Lave, 1988; Lave, & Wenger, 1991). From these perspectives “cognitive apprenticeships” are needed, akin to craft apprenticeships, in order to foster students’ ability to use the cognitive tools specific to a domain of practice (Brown et al., 1989, p. 39). Such apprenticeships will however, go beyond manual or physical skills, and attempt “to promote learning within the nexus of activity, tool and culture” (p. 40). Given that professional work practice placements are part of most forms of professional education, it is important to distinguish between learning on the job through training and practical experience, and learning through a cognitive apprenticeship model that promotes reflection in action by integrating theory and practice (Schön, 1983). Apprenticeship models that integrate hand and mind are in keeping with the “master craft” approach associated with the educational philosopher John Dewey (Chambliss, 1990, pp. 68, 114). Educational researchers at the Open University in the United Kingdom are concerned with fostering higher learning using Computer-Based Learning techniques (CBL) (Laurillard, 2002). A relational view of learning is considered so that learners 8 can apprehend the structure of a knowledge base interrelating theories; integrate theories and their abstract symbolic representations or “academic descriptions”; use academic descriptions to mediate between theory and practice; and reflect on the consequences and validity of their use in practice (Laurillard, 2002, pp. 48-61). In order to bring knowledge and experience together, it is claimed that learning must be mediated. First-order experiential knowledge of the world of objects, behaviours and sensations must be integrated with second-order knowledge of academic descriptions, or theories as represented in language, diagrams, pictures and symbols (Laurillard, 2002, pp. 21-22). Research is needed at the tertiary level it is suggested, into the way students handle and interpret representations (p. 48), map between “formalism and the reality it represents”, integrating language, mathematics, diagrams and symbols, with theories and practical activities (p. 52). Such integration is needed to cultivate academic understanding and take learning beyond the situation in work experience. Lemke (1998a, 2000) demonstrates that in scientific contexts, concepts are articulated across multiple forms of representations, including verbal language, graphs, equations and chemical symbols. More meaning is achieved, Lemke (2000) argues, if these multiple semiotic systems are integrated and coordinated in practice simultaneously; but students need assistance to acquire these “multi-literacies”, they are not acquired automatically (p. 267). Knowledge work in clinical chemistry is symbolic analysis. It entails the application of multi-literacies, the ability to inter-relate theories, integrate theories and practice, and to manipulate multiple representational formats such as graphs, charts and statistics, in order to interpret data from experiments. There is limited research into the way medical science education is conducted in Australia, at least research that characterises knowledge work and symbolic analysis in terms of multi-literacies. It is likely that the Competency-Based Standards for Medical Scientists (CBS-MS, 1993) derived from collaborations between government, universities, medical science professions and industry in the early 1990s, are to date, the most widely used intervention aimed at improving competency and professional standards in laboratory practice. One research project (Martin, 1997) comparing different Australian university work-based models, evaluated the effectiveness of work practice placements on students’ professional development. CBS-MS (1993) guided the questionnaire and interview data in the case of medical science education. Interviewees, work supervisors, academics and 9 students, concluded that CBS-MS were useful, especially given the absence of other clear guidelines for developing competencies in laboratory practice. No attention was given in this study (Martin, 1997) however, to the way students handle abstract symbolic descriptions, as is recorded in their university practical work. There would be limited access to this kind of data in work practice placements because data manipulations and calculations are performed using microprocessor algorithms in automated instruments. Also, things have changed since the early 1990s when CBS- MS were formulated, and a rethink of these competencies for clinical chemistry Mode 2 knowledge is now needed. This is because abstract symbolic descriptions, graphs, charts and statistics, apply not just to laboratory data and results validation, but also to laboratory test evaluations in EBLM (Muir-Gray, 1997; Shultz, 1999). This thesis aims to provide a theoretical framework for thinking about professional knowledge, knowledge work and symbolic analysis in clinical chemistry and the multi-literacies needed for operating effectively in computerised laboratories, and EBLM. This latter point is important because experts, although critical in disciplines, are not necessarily critical of them, and social accountability is a fundamental requirement in the production of transdisciplinary Mode 2 knowledge (Gibbons et al., 1994). Recent literacy studies set guidelines for the levels of literacy and competencies needed by workers in so-called new knowledge environments, computerised, globally competitive, and accountable in economic and social terms (Gee, 1996; Gee, Hull, & Lankshear, 1996; Gerber, & Lankshear, 2000). Operational, cultural (discipline or discourse specific) and critical literacies are needed, so that workers who are critical in discourses provide value-adding work performances keeping industries viable, and workers who are critical of discourses, ensure that they are socially accountable (Lankshear, 2000). Literacy studies are driven by a democratic impulse because it is argued, workers who are critical of discourses are instruments in cultural change, and in the process their life options and horizons are widened (Gee, 1996; Lankshear, 2000). Literacy studies specify fields and disciplines as discourses engaged in specific ways of speaking, acting, communicating and believing, and learners are inducted into the ways of a culture (Gee, 1996). Drawing on emancipatory literacy theory (Freire, 1972) and discourse theory (Bourdieu, 1979/1984; Foucault, 1969/1972), they encourage learners to think 10 for themselves, so that learners are enculturated not colonised by discourses (Gee, 1996). Literacy theory provides an overarching perspective of discourse and culture and of learning as enculturation into the knowledge and practices of discourses. Semiotics provides a specific theoretical framework and tool of cultural or discourse analysis, applicable to science cultures because of its strong connections with science philosophy and scientific method (Eco, 1976; Morris, 1971). A detailed explanation of semiotics will be provided in Chapter 4, but it is important to clarify the boundaries of semiotics at the outset. In broad terms, semiotics is a theory of signs and representations, signs being percept-concept (signifier-signified, expression- content) relations, that brings together the empirical world of sensations or experience and the rational world of the mind, concepts, ideas and theories, actions and feelings (Eco, 1976; Morris, 1971). Signs are sometimes considered in terms of representations that resemble (icons), point to (indexes) or stand in for (symbols) other things. From a scientific perspective, a sign requires the idea of a thing and its representation to be connected by an act of inference, a logical manoeuvre. Thus, signs are not icons, indexes or symbols, rather signs signify, iconically, symbolically and indexically, and to varying degrees (Peirce, 1931-58). The representative aspects of signs (percepts, expressions, signifiers) can be identified as significant units, for example words and syllables in natural languages. Signs coexist in relations in structures, and they can be reduced into distinctive units, such as letters and sounds, which are then recombined into different signs for classification into catalogues. From these activities, a theorist may discern an orderly pattern such as the grammar of natural language (Saussure, 1959). A similar process is demonstrated in natural history in which classificatory schemas of flora and fauna, based on morphological characteristics, have provided the basic structures leading to hypotheses about life functions in biology (Foucault, 1966/1970). Semiotics as a tool for analysing the sign systems of cultures, places signs and their representative aspects (signifiers) in structured relations with each other (syntactics); in semantic relations with the objects of signification, their intended codified cultural meanings (semantics), and in relations with interpreters who modify cultural meanings according to the contexts and circumstances in which they are considered (pragmatics) (Eco, 1976; Morris, 1971). The way things are said or represented, rhetoric (the form of expression), and the values that underpin the way things are 11 interpreted, ideology (the form of content), are also addressed by semiotics, which is ultimately a form of social criticism (Eco, 1976). Because signs are more than representations, there is more to multi-literacies than the manipulation of different forms of expression, and from a semiotic perspective knowledge work and symbolic analysis are interchangeable terms. From the semiotic perspective, the clinical chemist as knowledge worker isolates pertinent fragments of knowledge from a global semantic system of medical science knowledge for use in practice. In the process several things are accomplished: the interrelation of theories, within and between scientific knowledge systems (signified- signified relations); the interrelation of representations, symbolic and mathematical forms and theories (signifier-signified relations); the overriding of theory or rule- governed semantic connections by pragmatic circumstances such as availability of services, staff, space and budgets; acknowledgement that value judgments inform decisions when there are choices in clinical interpretations of laboratory tests and results; the juggling of multiple forms of scientific expression, verbal language, tables of data, graphs, charts and statistics, switching between them to extract different information as needed (signifier-signifier relations); and finally, recognition that rhetorical devices or “framing effects” are used in communicative situations to persuade people to favour particular interpretations over others. The clinical chemist as knowledge worker and symbolic analyst, recognising that statistical representations are vulnerable to manipulations favouring vested interests, adds value by guarding against misrepresentation of laboratory test information in EBLM. It is proposed in this thesis that semiotics provides a powerful tool for addressing the problems observed in clinical chemistry laboratory practice, and for facilitating the transition from clinical chemistry multidisciplinary knowledge to transdisciplinary Mode 2 knowledge in EBLM. This proposition will be tested on various clinical chemistry data sources.

1.4 Aims, research questions and objectives

Aims: Because semiotics has been proposed as a tool that can be applied to the teaching of contemporary clinical chemistry knowledge and practice, there are three broad aims driving this research. The first aim is to find a meaning for knowledge work and symbolic analysis in the “new knowledge” clinical chemistry laboratory 12 environment with the aid of semiotic theory. The second aim is to derive a semiotic framework from a wide range of semiotic theories, applicable to clinical chemistry knowledge and practice in automated computerised laboratories, and EBLM. The third aim is to demonstrate the effectiveness of the semiotic framework applied to clinical chemistry transdisciplinary (Mode 2) knowledge, and knowledge work and symbolic analysis in laboratory practice. In order to reach these aims the derivation of the semiotic framework (in Chapter 4) is guided by posing four research questions.

Research questions: Firstly, what structure of clinical chemistry knowledge applies to knowledge work and symbolic analysis in automated computerised laboratories and EBLM? Secondly, what contextual factors constrain knowledge work and symbolic analysis in automated computerised laboratories? Thirdly, what modes of reasoning do knowledge workers apply, and what adds value to clinical chemistry laboratory practice? Fourthly, what range of competencies do knowledge workers need for contemporary clinical chemistry practices, and what additional skills will add value in EBLM? The responses to these questions will be given by addressing nine objectives which help divide the research into manageable proportions, each objective tackling an aspect of the socio-cultural requirements for higher learning set forth in the rationale for this research (Section 1.3). These aspects will be addressed by semiotics, and include clinical chemistry knowledge structure and context, and the logic and rhetoric of laboratory practice.

Objectives: 1. To explore changes in clinical chemistry laboratories due to advances in automation, robotics and informatics, and the shift in work emphasis from manual techniques to computerised knowledge work and symbolic analysis. 2. To explore research into higher education in order to find a theoretical basis for knowledge work and symbolic analysis in the medical sciences. 3. To distil from a wide range of semiotic theories, a framework applicable to knowledge work and symbolic analysis in clinical chemistry. 4. To design semiotic analyses that answer the research questions by linking the theoretical framework with relevant clinical chemistry data sources. 13

5. To demonstrate the application of semiotics to the structure of clinical chemistry knowledge in the era of socially accountable laboratory medicine. 6. To demonstrate the application of semiotics to the analysis of the clinical chemistry laboratory context, in terms of the spatial arrangements and instrument designs that impact on the knowledge work experience. 7. To demonstrate the application of semiotics to the logic and multi-literacies used in clinical chemistry laboratory practice. 8. To demonstrate the application of semiotics to the rhetoric used in communicating laboratory test information, and to the values that underpin interpretations. 9. To propose further research and make recommendations for cultivating knowledge workers and symbolic analysts in the medical sciences, and to promote semiotics as a powerful tool for advancing higher education in the sciences in general.

1.5 Design methodology

This research is theory-driven, theory providing the “external scaffolding”, as Wolcott (1992, p. 25) explains, around which to structure clinical chemistry Mode 2 knowledge; and for the analysis of various data sources towards characterising knowledge work and symbolic analysis in laboratory practice. Semiotic theory as applied in this thesis is drawn from structural linguistics (Hjelmslev, 1943/1961; Saussure, 1959), literary theory (Barthes, 1964/1973; 1967/1990), science philosophy (Morris, 1971; Peirce, 1931-58), and their integration in Eco’s theory of semiotics (Eco, 1976). Research addressed specifically to scientific representations is also of interest. Lemke’s research (1998a, 2000) into the organisation, presentation and use of sign systems is of interest because it investigates the multi-literacies and multi-media semiotics used by scientists. Visual literacy, as investigated by Kress and van Leeuwen (1996), is considered for the analysis of textual, framing, foregrounding and vectorial effects in scientific diagrams. Latour’s (1987, 1990) “ethnography of inscription” is of interest because it examines the way scientific articles are constructed to attract financial allies for research projects. Scientific representations, equations, diagrams, charts, graphs and statistics, are inextricably linked with the structure of scientific knowledge and the logic and rhetoric of scientific practice. Science philosophers associate signs and representations with logic and pragmatics (Morris, 1971; Peirce, 1931-58), and in semiotics, signs and 14 representations are integrated in structures, logic and pragmatics apply to their use, and values and rhetoric apply to the way they are used (Eco, 1976). Eco (1976) provides an overarching semiotic theory of culture based on assumptions that “the laws of signification are the laws of culture”, so that cultures can be viewed as semiotic phenomena, analysed by their reduction to culturally significant entities or units, which get modified as they are used (p. 28). The theories of semiotics referred to in this thesis were derived before cultural analysis was subjected to interrogation as errors in cultural anthropology came to the surface, errors that were particularly associated with the treatment of research subjects and biased cultural interpretations (Denzin, & Lincoln, 2000). More recent perspectives on cultural analysis, as addressed in cultural studies, are therefore considered with respect to data collection techniques and ethical issues (Bennett, 1998; Frow, & Morris, 2000). The design methodology for the semiotic analysis of clinical chemistry, data sources, data collection and data analysis techniques and ethical issues, are explored in depth in Chapter 5. Access to the research setting, the teaching laboratory in the university and clinical chemistry laboratories in industry, has been made possible due to the researcher’s affiliations with the pathology industry, the profession (AACB), and participation as laboratory demonstrator in clinical chemistry laboratory classes. The applicability of the semiotic framework to clinical chemistry knowledge and practice is demonstrated using a wide variety of qualitative data sources - textbooks, course materials and journals; laboratory spatial arrangements; artefacts, laboratory instruments and printouts; and archival records of students’ practical work, including their data manipulations in graphs, charts and statistics (Creswell, 1998; Krathwohl, 1998; Yin, 1994). These data sources are analysed and reduced to manageable proportions from which samples are drawn “purposively” to demonstrate the logic of semiotic theory (Krathwohl, 1998; Miles, & Huberman, 1994). Three semiotic analyses are designed to demonstrate the effectiveness of semiotics applied to structure, logic and rhetoric in laboratory testing. The first analysis addresses the first and second research questions. Clinical chemistry knowledge is given a structure that places it in relations with other forms of knowledge. The laboratory context is subjected to spatial analysis that describes and compares it with industry laboratories; and designs of laboratory instruments are examined for the constraints they impose on knowledge workers’ laboratory 15 experience. The second analysis, addressing the third research question, draws on a knowledge fragment to demonstrate that semiotic sign logic underpins laboratory practice, in the classification of laboratory instruments, in ideal rule-governed use of instruments, and in the detection and diagnosis of errors. The way multi-literacies are used in laboratory practice is demonstrated drawing on error scenarios recorded in students’ practical reports. The third analysis, addressing the fourth research question, proposes the range of competencies needed for knowledge work when clinical chemistry is understood as a transdisciplinary knowledge system. This analysis demonstrates that knowledge workers override theoretical considerations in pragmatic circumstances; and that symbolic analysis of laboratory test information permits the extraction of different kinds of information from graphs, charts, and statistics; and also that rhetoric and ideology influence the way laboratory test information is communicated and interpreted.

1.6 Thesis layout

Chapter 1 introduces the research problem, poses the research questions, aims and objectives, semiotic theory and the semiotic analyses applied to clinical chemistry knowledge and practice. Chapter 2 reviews various sources of medical science literature to describe the changes taking place in the pathology industry; and to describe a shift in medical scientists’ roles from the performance of manual experiments towards knowledge work and symbolic analysis of experimental data and results with the aid of automation, robotics and computers. Chapter 3 presents a review of literature into higher education, connecting with the debates about the purposes of universities, the quality of science education, and the strategies used to improve higher learning, particularly in the professions. Particular attention is given to CBS-MS (1993) and extended forms of competency needed for transdisciplinary Mode 2 knowledge systems. This chapter provides insights into the nature of knowledge work and symbolic analysis, emphasising the importance in scientific fields, of finding ways to mediate between academic and experiential knowledge. Semiotics is proposed as a theoretical framework because it integrates theory and practice through the media of signs and representations. Chapter 4 explains semiotics, and distils the framework used to structure clinical chemistry knowledge, the laboratory context, logic and pragmatics in laboratory practice, rhetoric and 16 ideology in laboratory test communications, and the multi-literacies applied in each case. Chapter 5 locates semiotics within the more recent cultural studies research tradition which guides data collection techniques and ethical issues, and explains how in data analysis, the semiotic framework and data sources are linked to answer the research questions. Chapter 6, using documentary data sources, applies the semiotic framework to derive a substantive structure for clinical chemistry knowledge. It also describes the laboratory context, and explores the constraints placed on knowledge work and symbolic analysis by the designs of laboratory instruments. Chapter 7 demonstrates, using observations of laboratory instruments, printouts, and practical reports of experiments, the semiotic sign logic that applies in rule-governed laboratory practices, and the multi-literacies that underpin those practices. Chapter 8 outlines the range of competencies needed for clinical chemistry transdisciplinary Mode 2 knowledge, and demonstrates by adapting a laboratory test evaluation, that rhetoric and ideology apply in laboratory test reporting. Chapter 9 concludes the thesis, summarises the findings, addresses the contribution made to existing theory, knowledge and higher education, validates claims made, and makes recommendations for further research and future directions.

1.7 Conclusion

This chapter identifies problems in students’ laboratory practices in a clinical chemistry teaching situation, and argues that it is important to investigate these problems in the context of changes occurring in the pathology industry, higher education and research or knowledge production. There is insufficient evidence to explain why students demonstrate certain problems in laboratory classes, however, the problems observed have prompted thoughts about the nature of skilled work in automated and computerised laboratories, understood as knowledge work and symbolic analysis. Semiotics has been proposed as the theoretical framework needed for organising clinical chemistry professional knowledge, and for explaining logic and rhetoric in laboratory practice. Its applicability to clinical chemistry is demonstrated in an academic setting, and is likely to require modification for “real world” pathology industry settings. This thesis makes an original contribution to knowledge by applying semiotics, a well-established theory of logic, signs and representations, in the field of clinical chemistry, in which it has rarely, if ever been 17 tested. In achieving its purpose, this thesis contributes to the scholarship of research, the scholarship of teaching, and the scholarship of integration, as proposed by Boyer (1990) (see also Candy, 2000). The scholarship of integration is achieved by the application of a semiotic framework for knowledge work and symbolic analysis. This claim is made because a relational view of knowledge is considered, integrating different scientific and non-scientific disciplines, and theory and practice, the world of academia and the world of work, which come together in signs and representations. 18

Chapter 2 Medical science industry, work and profession

2.1 Introduction

The pathology industry has experienced dramatic changes in the last few decades due to advances in scientific knowledge and technology, automation, robotics and informatics. These changes have occurred in a climate of economic and social accountability, and are accompanied by changes in the nature of laboratory work. Medical scientists working in clinical chemistry laboratories perform far fewer manual techniques involving manipulations of pipettes, chemical reagents, test tubes, instruments, graphical manipulations of data and calculations, than they did a few decades ago. A large proportion of the work now involves computer-based data and results validation, quality monitoring, and clinical interpretation, and the work was referred to in the previous chapter as knowledge work and symbolic analysis. The purpose of this chapter is to explore the changes in clinical chemistry laboratory work accompanying advances in automation and computerisation, as a shift away from manual techniques towards knowledge work and symbolic analysis. Also explored are the moves towards Evidence-Based Laboratory Medicine (EBLM). The evolution of medical laboratory science, specifically clinical chemistry, is tracked over the last few decades in documentary evidence, in medical science and clinical chemistry journal articles, newsletters, conference papers, professional association websites, and clinical chemistry course materials. The first step in this process is to consider the way the medical science profession and medical science courses have kept pace with the changes.

2.2 The medical science profession

This section reveals the way the medical science profession and universities engaged in medical science education have responded to pathology industry transformations, through changes in professional titles and qualifications. Medical science in the Western tradition is engaged in medical interventions including drugs, diagnostic imaging techniques and laboratory testing, and to a lesser degree, social

19 and preventive medicine. Medical science as a career encompasses testing of biological specimens such as tissues, blood, and urine by qualified scientists and technicians, to assist in the diagnosis, prognosis, treatment and prevention of disease. In most countries medical laboratory science occupations are placed in the categories of scientist, technician and laboratory assistant positions (AIMS, n. d.). In Australia, the job description is “medical scientist” for those holding a Bachelors’ Degree (Bachelor of Applied Science [Medical Science]). The distinction between scientists’ and technicians’ roles was unclear before the introduction of the Clinical Laboratory Improvement Amendments (CLIA’88) in the USA (Ehrmeyer, 2000). This is because it was perceived that inadequate training was causing serious laboratory error and misdiagnosis of patients’ conditions (Barley, & Orr, 1997; Westgard, & Klee, 1999). Other countries followed this move in their own specific ways, in Australia for example, through laboratory accreditation by NATA (National Association of Testing Authorities) (NATA, n. d.) (see also Australasian Association of Clinical Biochemists [AACB], 2000b). This section focuses attention on the medical science profession, the Australian Institute of Medical Scientists (AIMS), which serves medical scientists working in all pathology laboratory disciplines, as distinct from the specialised professions, the Australasian Society of Microbiologists (ASM) and the AACB which also serve pathologists and research scientists. A pathology service in the Australian context is defined as “a service in which human tissue, human fluids or human body products are subjected to analysis for the purposes of prevention, diagnosis, or treatment of disease” (Farrance, 2000, p. 8). The distinction between body fluids, parts and products, and the different approaches to analysis underlie the distinctions between the various pathology disciplines. During the first half of the twentieth century, the pathology industry became clearly compartmentalised into a number of autonomous disciplines each utilising specialised and generally unrelated techniques (Rosenfeld, 1999). The clinical disciplines are characterised according to the following activities: Clinical Chemistry applies physical and chemical methods to the analysis of components in biological specimens, for example blood glucose for the diagnosis of Diabetes Mellitus, and cholesterol for the assessment Coronary Heart Disease (CHD) risk; Haematology studies the morphology of cells in blood to detect abnormalities such as iron

20 deficiency anaemia, vitamin B12 deficiency (pernicious anaemia), and the polycythaemias and leukaemias (malignant proliferation of red and white cells); Microbiology identifies pathogenic organisms, including bacteria (Bacteriology), viruses (Virology), parasites (Parasitology), and fungi (Mycology); Histology and Cytology examine the structure of tissues and cells respectively, to detect abnormalities such as neoplasms or cancers; Immunology (and Serology) focuses on antigen-antibody reactions of the immune response of the body, and related areas are Blood Blanking and Immunohistochemistry which study the compatibility of blood and tissues for transfusion and transplantation respectively (AIMS, n. d.). In Australia, medical scientists are trained in all aspects of the pathology laboratory, but tend to be associated with one area of disciplinary expertise due to preference or circumstance (employment opportunities). There are moves however towards multi- skilling (AACB, 1999a; Med Tec International 1996). In Australia, early in the twentieth century the job description for those employed in pathology laboratories was laboratory assistant or technician, and credentials were acquired following formalised technical college training that usually culminated in a technical association membership. The Society of Laboratory Technicians of Australia (1932) arose out of the Medical Sciences Laboratory Assistants Association (1923) (AIMS, 1996). In the post-war era of the mid-1950s, the process of professionalization accompanied technological progress, so that the training of medical laboratory assistants and technicians was geared towards medical technology. In order to reflect the changes in work, the technical association was renamed the Australian Institute of Medical Laboratory Technology (AIMLT) (AIMS, 1991, 1997). By the mid-1960s the training was transferred from Technical Colleges to the new Institutes of Technology and the credential conferred was a Diploma in Medical Laboratory Technology (DMLT). This move was closely followed in 1971 by another name change of the professional association to Australian Institute of Medical Technology (AIMT) (AIMS, 1991). By the mid- 1970s, Institutes of Technology began conferring Bachelors Degrees on newly graduating medical technologists (Bachelor of Applied Science [Medical Technology]). This process is recorded in the handbooks of the current participating universities (see AIMS, n. d.) that were formerly Institutes of Technology, for example QIT (Queensland Institute of Technology), now QUT (Queensland

21

University of Technology) (Kyle, Manathunga, & Scott, 1999) (see also Angel, Harden, McKenzie, & Moriarty, 2000, for the trajectory of courses at Charles Sturt University). The process of professionalization of medical science has been intermeshed like other professions, with the struggle to maintain jurisdiction and legitimation of professional knowledge (Abbott, 1988), and with technological progress, automation, robotics and informatics (Rosenfeld, 1999). Whereas professional jurisdiction and the legitimation of professional knowledge is a complex sociological problem, the latter technological issue is crucial for understanding the nature of laboratory work, and attempts by the professions to keep pace with the changes. The theories and instrumentation that emerged from the fields of physics and chemistry in the first half of the twentieth century permitted measurement of components of biological specimens using optical principles in spectroscopy and electrical properties in electrochemistry (Rosenfeld, 1999). In the mid-twentieth century, collaborations between the fields of physics and biology permitted new analytical techniques to transform the scene. In particular, the collaboration between Salomon Berson and Nobel laureate Rosalyn Yalow in New York City in the 1950s, led to the development of radio-immunoassay (RIA) for the measurement of antigen-antibody interactions (Rosenfeld, 1999, p. 478). The new measurement technique, immunoassay permitted the measurement of increasingly low concentrations of analytes such as hormones and binding proteins. This in turn allowed the field of Endocrinology (study of hormonal systems) to flourish and resulted in the rise of Immunology as an autonomous pathology discipline (see also Latour, & Woolgar, 1986). Immunoassay represents a watershed in pathology testing, a shift in the logic of chemical testing, although it was ultimately to become a complementary avenue of chemical analysis, not a substitute for those already existing. Another more recent watershed in pathology testing can also be traced back to the 1950s and the discovery of the molecular structure of nucleic acids by Nobel laureates Watson and Crick, in collaboration with many others (Levinovitz, & Ringertz, 2001). The collaborations needed for molecular diagnostics to function as a discipline are more complex than those initially involved in the inception of immunology. Molecular diagnostics incorporates the activities of disparate fields in addition to physics, chemistry and biology, including informatics and legal and

22 business activities such as commercialisation, marketing and patenting (Gibbons, Limoges, Nowotny, Schwartzman, Scott, & Trow, 1994). This transformation in knowledge production was referred to in Chapter 1, as a shift in the mode of knowledge production from Mode 1, specialist, disciplinary, scientific knowledge produced in research universities, to Mode 2, transdisciplinary knowledge produced by collaborations between universities, private industries, government research institutions, and the sites of application such as hospitals (Gibbons et al., 1994). Accompanying this transformation in knowledge production it was also noted that there is a blurring of boundaries between the pure and applied sciences, and between clinical pathology and medical science research. The developments in automated technologies and immunoassay kits and instruments began an era of rapid change in the pathology industry that has been escalating since the 1970s. The skills used by the clinical chemist in the 1970s and 1980s were focussed on the use, maintenance and correction of complex methods and instruments, and the buzzword “troubleshooting” introduced at the turn of the twentieth century, applied to the correction of faults in telephone line equipment and machinery (Barnhart, 1988), increasingly entered into laboratory parlance. The technological advances that helped to resolve the inherent contradictions between achievement of high quality results on large numbers of specimens in shorter times, between the speed of analysis and its accuracy and precision, were matched with advances in control quality (QC) using increasingly sophisticated statistical techniques (Rosenfeld, 1999, p. 484). Analytical QC however, had to be more broadly conceived to account for many other sources of error, so the more encompassing terms “quality assurance” (QA) and “quality management” (QM) were introduced to account for everything and everyone in the laboratory (Westgard, & Klee, 1999). As laboratory management became an avenue of career progression for medical scientists (AACB, 1998b), they joined ranks with other professionals who sought the Masters of Business Administration qualification (MBA) introduced by business schools in Institutes of Technology and Universities in the 1980s (Clout, & Marshall, 1995). The increasing complexity of work in clinical pathology was reflected in a name change of the professional association to the Australian Institute of Medical Laboratory Scientists (AIMLS) in 1978 (AIMS, 1991). The medical technologist

23 became a medical laboratory scientist and the degree conferred was Bachelor of Applied Science (Medical Laboratory Science). Because the acronym AIMLS acquired the unfortunate connotation “AIMLesS”, the name that stands at the turn of the twenty-first century is that adopted in 1991, in which the word laboratory is deleted, that is Australian Institute of Medical Scientists (AIMS) (AIMS, 1991, p. 3). At much the same time many of the universities participating in medical science education modified the qualification title to Bachelor of Applied Science (Medical Science), or simply Bachelor of Medical Science. The fact that the terms “laboratory” and “applied” have been removed reflects the flexibility and mobility needed by medical scientists for career advancement (Angel et al., 2000). In the last decade of the twentieth century, the advances in technology and informatics have significantly compounded to such an extent that professional changes seem to be relatively minor. As Abbott (1988) argues in his analysis of the system of professions, there are now much stronger forces at work than those that steered professionalism on its course (p. 325). Less attention is needed on individual professions and more attention on industry factors that are uniting the disciplines and transforming work, which as Abbott observes, is becoming increasingly abstract and symbolic (p. 55). This view has wide support from analysts of professional work in technical computerised environments (Aronowitz, & Di Fazio, 1994; Barley, & Orr, 1997; Drucker, 1993; Gerber, & Lankshear, 2000; Gibbons et al., 1994; Reich, 1992), and is pursued further in this Section 2.4 following more detailed description of changes occurring in the pathology industry.

2.3 Transformations in the pathology industry

The pathology industry is rapidly evolving due to new knowledge in molecular biology, technological developments in instrumentation, automation, robotics and methods, and informatics, laboratory information systems and diagnostic Expert Systems. There is a dialectical interplay between technological development and the expansion of new scientific knowledge (Rosenfeld, 1999). As these technological and scientific advances occur, the pathology industry is also caught up in micro-economic reforms of the health sector as part of the general push towards economic globalisation (Farrance, 2000). The evolution of the pathology

24 industry and technological advances in laboratory instrumentation, are thus partly a response to economically rational objectives to “rationalise”, “downsize”, and “reengineer”, so that laboratory testing is driven by economic and political imperatives as much as it is technologically driven (Lincoln, 1996; Rosenfeld, 1999; Weiss, & Ash, 1999; Wilding, 1998). Two broad pathology industry developments are considered in this section because they have bearing on the design of medical science courses. They are placed under the general headings, technology incorporating computers, and evidence incorporating ethics and values. The first major issue to consider is technological advancement, encompassing large scale automation and robotics, molecular techniques, and micro-fabrications permitting miniaturised testing at the point-of-care, point-of-care testing or POCT (AACB, 1998a, 1999a, 1999c, 2002a; Kost, 1996). Despite the diversity of these technological processes, they are orchestrated by laboratory information systems that interface between laboratory results, information about patients and specimens, reporting and billing, incorporating databases and diagnostic Expert Systems (AACB, 1999a; Sikaris, 2001). From an organisational perspective, developments in technology are dividing the pathology industry roughly in three ways, into large scale highly automated, in some cases totally automated centralised laboratories; molecular diagnostics and genotypic testing; and micro-scale decentralised POCT (Wilding, 1998). Large scale automated laboratories have evolved as a response to the challenge by government funding bodies to achieve more with less, by a process of laboratory “reengineering” (borrowing the term from the corporate world of business) in which laboratory designs and spatial arrangements optimise workflow, and improve productivity, efficiency and quality (Weiss, & Ash, 1999). Reengineered laboratories around the world are organised according to open plan spatial arrangements made possible by new technologies that permit different approaches to analysis on one instrument. These new technologies are largely responsible for the blurring of boundaries between the disciplines, particularly clinical chemistry, immunology and haematology (Lincoln, 1996), and increase the requirement for multi-skilling by medical scientists (AACB, 1999a; Med Tec International, 1996).

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Molecular diagnostics has emerged as a new pathology discipline due to the combined activities of disciplines physics, chemistry, biochemistry, molecular biology, and microbiology working in collaboration with diverse groups in business, engineering and computing, in biotechnology projects such as genotyping and genetic engineering (AACB, 1999c). The biotechnology industry is a producer of transdisciplinary Mode 2 knowledge because it conducts a diverse range of research activities through cooperative endeavours between disparate fields, governments, industries and universities (Gibbons et al., 1994; Gibbons, 1999). In Australia for example, the Cooperative Research Centre for Diagnostic Technologies (CDT) operates according to a close alliance between government health organisations, biotechnology companies and universities (CDT, n. d.). The biotechnology industry is as much a business venture engaged in commercialisation, marketing, and patenting, as in computing, molecular biology, and genetics. University biotechnology courses are now tailored to meet the needs of the biotechnology industry. For example Bachelor of Biotechnology Innovation (Queensland University of Technology [QUT]) currently offers the units: “Research development and commercialisation strategies”, “Innovation and market development”, “Venture skills”, and “Business and biotechnology” (QUT Handbook, n. d.). New methods and new technologies developed in biotechnology provide the potential to shift the pathology testing logic away from disease diagnosis, prognosis and treatment in testing for phenotypes characterised by lifestyle and environments, to disease prediction and prevention in testing for genotypes based purely on genetics. Such testing will be conducted increasingly at the point-of-care (AACB, 1999c; Price, & Hicks, 1999; Wilding, 1998). The pathology and biotechnology industries are thus coming together in molecular diagnostics and POCT. There is an international trend towards miniaturised laboratories in POCT at sites other than centralised laboratories made possible by microchip technologies (Kricka, & Wilding, 1996; Wilding, 1998). POCT using small bench top and hand held analysers is well established in emergency and intensive care wards as critical care testing, and in home glucose monitoring of Diabetes Mellitus (Price, & Hicks, 1999). POCT is being extended to a diverse range of activities, to support occupational health and safety in the workplace, for illicit drugs testing at work and in prisons, and on sports fields where blood lactate monitors facilitate athletics

26 training regimes. Whereas coronary heart disease (CHD) risk assessment has been conducted through cholesterol testing in shopping centres and other community centres for roughly two decades, the concept of public health and disease prevention clinics or “wellness clinics” is more recent. Molecular diagnostics screenings will eventually be incorporated into wellness clinics for disease prediction and prevention (Bais, & Burnett, 1999). POCT is problematic if it is placed in the hands of personnel untrained in analytical procedures and QC required for laboratory accreditation (AACB, 1998a; AACB, 2002a). Whatever the problems however, proponents of POCT argue that it is an “enabling technology” and “adds value” to pathology testing in terms of care and convenience for patients and physicians (Keffer, 1999, p. 236). Internet access guided by “connectivity standards” now allows these limitations to be addressed because POCT can be linked into central laboratory QC monitoring systems (Jones, 1999; St John, 2001; St John, & Ward, 2002). Home glucose monitoring, self testing and self management, and oral coagulation therapy are now being facilitated by companies such as “e-Medical Monitor” in this way (St John, & Ward, 2002). Ultimately the goal of research and development into micro-machines is to transform near-patient testing into in-vivo or in-patient testing. The potentials of in- patient devices such as “optodes” (St John, 1997), nanotechnology and the “lab-on-a- chip” are as yet unrealised in routine clinical laboratory testing (Jane, 2000; Wilding, 1998). Nanotechnology has been on the agenda since Feynman’s seminal paper (Feynman, 1959), which was inspired by the DNA example of writing information on a very small scale. Feynman considered the possibility that small machines could be developed for placement inside the body to target and treat disease. Nanotechnology has since then intrigued the world of science fiction (Drexler, Peterson, & Pergamit, 1991), and more recently it has been placed at the forefront of the next industrial revolution (Balogh, Tomalia, & Hagnauer, 2000). There is as yet no biologically functional nano-machine, however, microfabricated devices are being produced on increasingly small scales, and advances are being made with the “lab- on-a chip” which will perform many tests simultaneously using minimal space and reagents (Jane, 2000; Wilding, 1998). The implications of the lab-on-a-chip, and near-patient and in-patient testing, are uncertain with respect to medical scientists’ career prospects. On the one hand there is the potential for increased work

27 opportunities in POCT, and for research and development into micro-machines in a pathology industry and biotechnology industry merger (Wilding, 1998). On the other hand, there may be fewer opportunities for medical scientists as Internet access increases the potential for non-medical scientists to perform POCT, in particular general medical practitioners (AACB, 2002a). The second major pathology industry issue to consider with implications for medical science education is evidence, incorporating values and ethics. This is because the pathology industry worldwide is required to demonstrate the value of pathology services for clients, patients, physicians, and communities. Evidence is needed that laboratory tests are valid, appropriate, cost-effective and clinically relevant, and this evidence will be produced in EBLM (McDonald, & Smith, 1995; Morris, 2000; Muir-Gray, 1997; Price, & Hicks, 1999; Price, 2001). In Australia particularly since the 1980s, micro-economic reforms of the health sector have led to restrictions on Medicare payments to the pathology industry under the Medicare Benefits Schedule (MBS), in a bid to eliminate over-servicing and fraud (Farrance, 2000). There are also increasingly complex social and ethical issues in pathology testing that must be addressed, particularly associated with infectious diseases and genetics (AACB, 2002b, 2002c). EBLM is in the early stages of development in Australia, but more than likely it will be modelled along similar lines to Evidence- Based Medicine (EBM) (Muir-Gray, 1997; Sackett, Richardson, Rosenberg, & Haynes, 1997). EBM is well established internationally, for evaluating medical interventions such as surgical procedures, radiological imaging and drugs, to assess their cost- effectiveness, appropriateness, and clinical relevance. EBM conducts these evaluations by appraising existing evidence and research using vast data bases of existing patient results and by systematic reviews of existing research (Muir-Gray, 1997). In EBM the attention is placed on clinical relevance, by comparing medical interventions with “gold standards”, and on systematic reviews of existing research in random controlled trials (RCT), and cohort studies (Muir-Gray, 1997). Similar approaches will be used in EBLM, and similar graphical and statistical techniques. There is a much broader movement termed “Evidence-Based Health” (EBH) which accommodates much wider economic, political, moral, and social perspectives, taking EBM beyond the confines of medical science discourse, by juxtaposing it with

28 alternative health perspectives (Moynihan, 1998; Popay, & Williams, 1998; Stevens, & Milne, 1998) (EBLM is discussed in more detail in Section 6.3.4.2). In addressing EBM and EBLM, it is emphasised that the appraisal of laboratory tests requires much more than technical skill. The critical evaluation of data, results and medical information requires the ability to interpret complex graphs, charts and statistics for their scientific content, and also the ability to make value judgements and to tackle complex ethical issues. In summary, in the new knowledge era, medical scientists are required to be multi-skilled in the pathology disciplines; to be critical of data and results in quality monitoring; and to interpret results for their clinical significance. With these skills medical scientists should be well prepared for laboratory test evaluations in EBLM, the assessment of the clinical relevance, cost-effectiveness of laboratory tests, and their appropriateness in social and ethical terms. There is also the potential for medical scientists to engage in research and molecular diagnostics, laboratory informatics, and information theory (AACB, 1999c; Elevitch, & Spackman, 1999). Knowledge work in the medical sciences involves symbolic analysis of tables of data, complex graphs, charts and statistics with the aid of computers and diagnostic Expert Systems (Sikaris, 2001). Some analysts suggest that professional jurisdictions will ultimately be challenged by Expert Systems as they take over many professional expert functions (Abbott, 1988, p. 182). Others say that Expert Systems will assist human experts to work more efficiently (Bedard, & Chi, 1992; Gillies, 1996; Sikaris, 2001). In either case, it is important to clarify the meaning of knowledge work and symbolic analysis in medical science education, for computerised work, and to see how symbolic analysis also applies to social criticism in socially accountable EBLM.

2.4 Knowledge work and symbolic analysis in clinical chemistry

In the past fifty years medical scientists have experienced sweeping changes to their roles and conditions of work in pathology laboratories. Considerable layers of complexity have been added to the tasks assigned to medical technologists in the 1950s and 1960s. This section explores the transformations in clinical laboratory work from the perspective of clinical chemistry with a view to isolating the core components of knowledge work in expert professional practice. There are

29 fundamental basic scientific principles in the knowledge that underlies clinical chemistry activities, despite the continual emergence of new scientific knowledge and technologies. Major transformations in clinical laboratory work have resulted from new medical science knowledge, automation, robotics, informatics and miniaturisation, and they come together in pre-packaged kit methods and molecular diagnostics. Whereas automation, robotics and informatics make it possible to “operate” reasonably effectively in the laboratory, scientists are interpreters who critically evaluate every aspect of laboratory work. Work effectiveness in clinical chemistry requires the focus of attention being placed on both “technical/analytical” and “biological/medical” spheres (Rosenfeld, 1999, p. 514). Clinical chemistry is defined by the field as “the application of chemical, molecular, and cellular concepts and techniques to the understanding and evaluation of human health and disease” (Athena Society, 1996, p. 99). It represents the intersection of technology and many disciplines in the physical and biological sciences and mathematics (Rosenfeld, 1999, p. 515). Many concepts and practices intersect in the analytical systems used for measurement of elements and compounds in biological fluids towards detection, diagnosis, and disease monitoring. Laws and principles from physics are applied in automated and non-automated laboratory instruments, and the chemical bases of methods must be described for all experimental situations. Mathematical and statistical rules are essential for data handling, calculations, and their evaluations, because the data collected from instruments must be assessed to ensure they are reliable and valid. The acceptance of results leads to their clinical interpretation based on clinical knowledge of anatomy, physiology, and biochemistry. This is accomplished by the comparison of test results with reference individuals according to statistical population “norms” or reference ranges (Solberg, 1999). Further scrutiny of test results is needed to ensure that each patient’s results are coherent, so that groups of results on individual patients do not present conflicting evidence for a diagnosis. The mismatch of results in this way is a tell-tale sign that technical or other type of error has occurred (see Section 7.3.3.4). The early skills-requirements for those working in clinical chemistry laboratories can be described as craft-based, understood in this case as involving manual techniques (and intellectual), the performance of chemical assays and their measurements in analytical instruments, calibrated manually at the bench (Barley, &

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Orr, 1997). In the assay of blood components (analytes), for example glucose, cholesterol, and urea, the analyte of interest was first prepared by the removal of interferents such as protein, before chemical reactions were performed in test tubes using specific chemical reagents. Medical scientists performed such assays in test tubes using manual pipetting techniques. The reaction mixtures were then measured on instruments designed to detect a specific response, typically photometric (measurement of the interactions between the element or compound and light) or electrochemical (measurement of electrical properties of the analyte itself) (Rosenfeld, 1999). Photometric responses were converted to electrical signals and recorded by electrometers, so that data were presented in graphical format by the instrument. Alternatively a direct readout of numbers was given and plotted on graphs manually by the scientist, who then calculated results into concentration units, and handed them on to pathologists and physicians who assessed their clinical significance. Because automation, robotics and Expert Systems now perform many of these functions, in order for clinical laboratory work to be more than machine- minding, the performance of automated instruments must be monitored by scientists. Data and results must be assessed for their accuracy and precision, patients’ test results for their clinical significance, and groups of result on individual patients for their diagnostic coherence. Interpretation is the key aspect of laboratory work that justifies the job description “medical scientist”, as is illustrated in the list of tasks required for completion of the AACB members’ examination (Appendix A.). In the performance of a chemical analysis, automated or manual, the medical scientist has the capacity to make decisions based on performance criteria, about the appropriate mode of testing with respect to instrumentation and method. In many cases performance data about expected accuracy (“right” answer) and precision (repeatable result) are given with instrument manuals and assay kits. If a new assay is to be developed, a medical scientist has the capacity to assess the suitability of instruments and methods by assessing their performance in studying a range of criteria associated with accuracy and precision (Westgard, & Klee, 1999). Also considered are appropriate standardisation or calibration procedures; data manipulation by application of mathematical curve fitting techniques; and clinical interpretation of results by their comparison with statistical reference ranges (population “norms”), according to

31 anatomical, physiological and biochemical principles. These activities are overseen by QC and QA procedures to ensure that results are valid at pre-analytical (control of errors in specimen collection, handling and transportation); analytical (control of errors associated with methods and instruments); and post-analytical (control of errors associated with reporting and results interpretation) levels (Westgard, & Klee, 1999). QC and QA are still not enough. As the pathology sector became increasingly accountable to government funding bodies and clients, it had to contain costs by increasing productivity and efficiency at the same time as improving quality. QA systems were thus expanded into more encompassing quality management systems (QM) (AACB, 1998b, 1999b; Weiss, & Ash, 1999; Westgard, & Klee, 1999). In Australia, individual laboratory QM processes are overseen by the regulating body NATA (NATA, n. d.) and managed by computerised auditing systems such as the ISO 9000 series (International Organisation for Standardization) (ISO, n. d.). As QM became an integral aspect of the knowledge and skills required in the clinical chemistry laboratory, business management became a suitable avenue of study for ambitious medical scientists, as earlier discussed. Because of the high volume of data, results and information turned over daily in automated clinical chemistry laboratories; heightened clinical understanding by medical scientists became a major concern for QM. In addition to managerial expertise and clinical interpretive skills, the medical scientist is aware of the limits of laboratory testing at the clinical decision level. Limitations are imposed on laboratory test interpretations due to intra and inter-individual variability that is, variability over time in testing for the same analyte, both within the same patient, and between patients, due to variations in seasons, activity, and long term health and emotional factors (Fraser, 2001; Kringle, & Bogovich, 1999). This awareness combined with expertise in statistical techniques acquired through analytical QC procedures, places medical scientists in a strategic position to tackle the complex questions that will be raised in EBLM, as laboratory tests are evaluated for their clinical relevance, appropriateness and cost effectiveness (Morris, 2000; Price, 2001). In summary, automation and robotics have provided improvements in accuracy and precision, which has in turn attracted more sophisticated QC models and statistical techniques. In addition, the techniques of immunoassay which elicit

32 more complex responses than the linear responses technologists had come to expect from electrochemical and photometric assays, require more sophisticated data reduction and mathematical modelling techniques (Nix, 1994; Raggatt, 1997). As analytical QC was expanded into QA and QM in attempts to reign in problems from all aspects of pathology testing, proficiency testing for comparisons among laboratories led to even more sophisticated statistical and graphical techniques (Westgard, & Klee, 1999). The wide range of activities involved in data, results and test interpretations has become so unwieldy that it is increasingly orchestrated by laboratory information systems and Expert Systems (Elevitch, & Spackman, 1999; Sikaris, 2001). It is evident in this range of scientific activities that the skills-base of the clinical chemist has shifted from manual manipulations of chemical methods and instruments in the earlier “craft” model which emphasised troubleshooting instrument malfunctions and routine maintenance, towards manipulation of increasingly complex representational forms of data, graphs, charts and statistics using computers. This shift in the nature of work along the manual-mental continuum in laboratory practice can be considered from two perspectives, the specific opinions of pathology industry professionals, and the general perspectives of sociologists and other analysts of work who have characterised work in highly computerised environments. Taking the first perspective of work from industry professionals, being focussed on productivity and efficiency issues, economic considerations tend to dominate in thinking about scientists’ roles in reengineered laboratories. Sikaris (1997) summarises the transformations in clinical chemistry in terms of a shift from a “craft group” to a “profession” to a “manufacturing business” whose “technical advancements are driven by the commercial sector”. Survival of a pathology laboratory today depends on its ability to execute the most number of tests with optimal precision and accuracy, on the most patients, at the fewest workstations, taking up the smallest areas of laboratory space, in the shortest time, at the lowest cost, and using the least number of operators (Isaacs, 1999). The reengineering process applied to accomplish this ideal, at the same time transforms the work, which is increasingly organised according to sections based on technology rather than on disciplinary knowledge, thus eroding the boundaries between the medical science disciplines. In the United States of America (USA), the specialist job description for

33 the clinical chemist has been reconsidered as “clinical laboratory scientist having cross-disciplinary expertise in analytical techniques and automation, which are the common threads linking all branches of clinical laboratory science” (Athena Society, 1996, p. 96). In addition to these scientific and technological activities, the American Association of Clinical Chemists (AACC) identified competency areas in need of attention (AACC, 1996). These competencies include clinical skills, test logic and test appropriateness, consultation and management skills, multidisciplinary team building and leadership. Because the evaluation of laboratory tests applies not just to their cost effectiveness but also to their clinical relevance, the pathology industry is expected to demonstrate the direct contribution pathology testing makes to patient outcomes, in improvements to health and the alleviation of sickness (McDonald, & Smith, 1995). In Australia, EBLM is still quite new despite the longstanding activities of EBM (Muir-Gray, 1997; Price, & Hicks, 1999; Price, 2001), and is recognised as an omission from the clinical chemistry curriculum (Morris, 2000). Taking the second perspective, work in computerised environments is defined by sociologists and other analysts of work as knowledge work (Drucker, 1993) and symbolic analysis (Reich, 1992). Knowledge workers help keep industries competitive and economically viable in the “new work order” of “post-capitalist” or “post-industrial society” (Aronowitz, & Di Fazio, 1994; Drucker, 1993; Gee, Hull, & Lankshear, 1996; Gerber, & Lankshear, 2000; Gibbons et al., 1994; Reich, 1992). To clarify the meaning of knowledge work and symbolic analysis in the laboratory, it is useful to consider the work of medical scientists by placing it within three broad and distinctive categories of work outlined by Reich (1992). These categories are routine production, person to person services, and symbolic analysis, the manipulation of data and symbols using computers (p. 179). For forty years, Drucker (1993) has been tracking transformations in work and the emergence of a “knowledge society”. He notes that in many professional technical domains, the nature of work has been shifting from manual to intellectual knowledge work (p. 6). This transformation accompanies a shift in the mode of production from “economies of scale” in which there is mass production of units at decreased costs, towards “economies of scope” in which businesses respond to global competition and consumer demand in order to remain afloat (Gibbons et al., 1994, p. 51). Economies of scope are concerned less with mass production than with the reconfiguration of existing products (and

34 knowledge) in novel ways, and this is made possible by the “value-adding” performances of “smart workers” (Lankshear, 2000). Beyond productivity and efficiency, smart workers add value through innovation, evaluation, optimisation and revision of existing practices (p. 114). In technical professions such as the sciences, architecture and engineering, this is accomplished by symbolic analysis, the manipulation of data and symbols using computers (Reich, 1992). There are few analyses of work in the medical laboratory sciences. In one American study of the technical labour force (Barley, & Orr, 1997), attention is directed towards medical laboratory technology, which is characterised by both practical (technical) and interpretive (intellectual) components. A general qualitative shift is noted towards “technization” of professional and technical work in general (p. 5). Work is increasingly complex and analytic, involving manipulations of “abstract symbolic representations” of physical phenomena, so that representations are tools that “mediate between workers and the objects of their work” (p. 5). Skilled laboratory work from this perspective is directed towards the minimization of laboratory error and requires “interpretive skills, troubleshooting machine malfunctions and improvisation” (Barley, & Orr, 1997, p. 199). The complexity of work is however being compounded as diagnostic Expert Systems take on many of the intellectual functions of medical scientists (Sikaris, 2001). The practical aspects of experiments involving the manipulation of objects, specimens, reagents, pipettes, test tubes and calibration of instruments, are performed by automated instruments and robotics. The intellectual aspects of data and results interpretation for quality, validity and clinical significance, troubleshooting, detection, diagnosis and correction of errors, are increasingly being performed by diagnostic Expert Systems, but must be monitored by medical scientists (Sikaris, 2001). Analysts of work have debated the potential for de-skilling in computerised automated environments, but conclude that for professions such as the medical sciences, skills are enhanced not decreased due to the increased complexity of computerised applications (Gibbons et al., 1994, p. 132). Aronowitz and DiFazio (1994) note that in professional technical environments, the computer is “Janus faced” because it produces a two-way work situation. On the one hand, there is a need for highly skilled workers, and on the other hand there are fewer highly skilled positions available (p. 103). The AACB (1999a) supports this assessment in the expectation that traditional roles will

35 continue to diminish and that fewer scientists will supervise more technical assistants. Industry managers stress that the medical science qualification is no longer a rite of passage to the secure career it once was, but a commencement to lifelong learning (Badrick, 2002; Wilding, 1995). For this reason, the requirement for continuing professional development is built into the professions’ code of ethics (AACB, 2002b, 2002c). Nonetheless, despite the appearances that prospects are diminishing due to automation, robotics and informatics, there are avenues of career advancement for medical scientists within and outside the medical science profession. Within the medical sciences, the potential for career advancement is limited. There are few possibilities in laboratory management, organisational and quality management, and in innovation, evaluation, optimisation and revision of methods (AACB, 1999c). Wilding (1998) suggests that large-scale automation, although leading to smaller workstations with fewer operators, will free up the scientific workforce for new activities, for research and development into micro-fabricated devices for use in molecular diagnostics (AACB, 1999c) and POCT (AACB, 2002a). Multi-skilling is needed for centralised automated laboratories and POCT, although the long-term implications of laboratory reengineering for medical scientists and their jurisdiction over POCT remain far from clear. Recent discussions indicate that POCT will be placed mainly in the hands of general medical practitioners (AACB, 2002a). There are alternatives to work in the clinical laboratory sciences. A medical science qualification provides excellent preparation for entry into the Graduate Medical Program (GMP) offered at a number of Australian universities (e.g. GMP, n. d.). There are also opportunities for postgraduate studies in genetics, bioinformatics, biostatistics, law and forensics (see QUT Handbook, n. d.), or alternative careers in secondary science teaching, science communication, nutrition science and environmental toxicology (see Kreeger, 1999; Tobias, Chubin, & Aylesworth, 1995). There is untapped potential for work in EBLM, as evidence accumulates in laboratory test databases that await evaluation based on healthcare outcomes, and existing published research that awaits systematic review (Muir-Gray, 1997; Price, 2001). Because EBLM and molecular diagnostics bring ethical issues to the surface, additional skills in value judgements are required in addition to those acquired

36 through training in techniques and acquisition of disciplinary knowledge (AACB, 2002b, 2002c). A full evaluation of the status of medical scientists’ roles in the pathology industry and further directions requires a comprehensive analysis of work. Such an investigation would be conducted for example by an ethnographic study involving extensive time in the industry, including interviews with laboratory managers and medical scientists for their perceptions of work (e.g. Latour, 1987). This thesis aims to provide academic perspectives by placing knowledge work and symbolic analysis on a theoretical footing, untroubled by the “noise” or “interference” arising due to productivity and efficiency issues at work. This is in order to address the challenges faced by the higher education sector, of cultivating knowledge workers, symbolic analysts, and lifelong learners, who are equipped for transdisciplinary Mode 2 knowledge work. Knowledge workers in the medical sciences are not just critical in the disciplines but are also critical of the field. They can evaluate tests in EBLM, other Health Technology Assessments (HTA), or engage in other lines of work.

2.5 Conclusion

A review of medical science literature in this chapter has tracked the evolution of the pathology industry and the reengineering of laboratories, due to automation, robotics and informatics. It was noted that as these changes are occurring, the nature of the work of medical scientists, specifically, clinical chemists is also changing. This shift has been characterised, in keeping with sociological analyses of work in computerised technical environments, as a shift along a continuum from manual technical work to knowledge work entailing symbolic analysis of abstract symbols with the aid of computers. There is a need to expand on notions of competence in the new knowledge environment of clinical chemistry, and to consider the competencies needed for socially accountable laboratory medicine, in EBLM. In the next chapter, knowledge work and symbolic analysis in clinical chemistry are placed on a theoretical footing, following considerations of the perspectives of universities, how they are transforming in the same climate of economic and social accountability that the pathology industry is experiencing.

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Chapter 3 Medical science in the context of higher education

3.1 Introduction

This review of literature into higher education explores the way universities are changing in response to global economic conditions and communications technologies that transform the way teaching and research is conducted. It considers the challenges faced by universities as disciplinary and multidisciplinary knowledge systems are being subsumed within transdisciplinary knowledge systems in order to address complex economic, environmental and social as well as scientific twenty- first century problems. The discussion is directed towards the purposes of universities, and the higher education reforms and strategies that are relevant to medical science education, particularly in clinical chemistry. Competency and workplace learning are considered because Competency-Based Standards for Medical Scientists (CBS-MS, 1993) are used to guide medical science students’ work experience in pathology laboratories. CBS-MS and work practice placements can ensure that medical science graduates have the operational competence needed to meet industry requirements. Taking a socio-cultural perspective however, training for operational competence does not necessarily acculturate students into the ways of medical science culture, or cultivate criticism of medical science discourse, as is required in Evidence-Base Laboratory Medicine (EBLM). Laboratory work practices now involve fewer manual activities and more knowledge work and symbolic analysis, computerised data analysis, quality monitoring and clinical interpretation; and the pathology industry is required to practice socially accountable laboratory medicine, in EBLM. This review converges on research into literacy, discourses and cultures in order to find a theoretical basis for knowledge work and symbolic analysis applicable to work in automated computerised laboratories in the medical sciences; knowledge work for learners as well as employers. It begins by exploring transformations in the way knowledge is produced with the purpose of demonstrating that professional medical science knowledge encompasses disciplinary, scientific, and transdisciplinary perspectives incorporating economic and social issues.

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3.2 Systems of knowledge production in higher education

The university in the Western world has been subjected to reinvigorated debate in the last two decades, and one of the main points commentators make is that the university no longer has the monopoly on knowledge production it once had (Gibbons, Limoges, Nowotny, Schwartzman, Scott, & Trow, 1994; Lyotard, 1979/1984). Various higher education researchers have been led to consider what kind of university is needed to account for global competitiveness and information and communications technologies which are inextricably linked with disciplinary forms of knowledge (Boud, 1998; Boud, & Solomon, 2001; Brennan, Fedrowitz, Huber, & Shak, 1999; Cunningham, Tapsall, Ryan, Stedman, Bagdon, & Flew, 1997; Gerber, & Lankshear, 2000; Gibbons et al., 1994; Laurillard, 2002; Symes, & McIntyre, 2000). The purpose of this section is to use the research literature examining knowledge production in universities and elsewhere, to support a central argument in this thesis, that the education of medical scientists for the “new knowledge” era requires more than training in laboratory techniques. It requires the cultivation of “knowledge workers” and “symbolic analysts” who can help keep the pathology industry economically viable, by value adding work performances in evaluation, optimisation and revision of methods, and who can meet the demands of socially accountable laboratory medicine, in the evaluation of laboratory tests in EBLM. More than two decades ago French philosopher Jean-François Lyotard (1979/ 1984) hypothesised in his Report on Knowledge that “the status of knowledge is altered as societies enter what is known as the postindustrial age and cultures enter what is known as the postmodern age” (p. 3). This transformation, underway since the late 1950s, he argued, was a threat to universities as they were conceived in the modern age. There are many commentaries on post-industrial and post-capitalist society (e.g. Bell, 1974; Reich, 1992), but Lyotard’s report is of particular interest because of his strategic projection of the status of scientific knowledge due to computerisation and technological advancements which have transformed the biomedical sciences, especially genetics (Lyotard, 1984, p. 4). The legitimation of scientific research, its justification and validation (and also education) is considered. Lyotard explains two broad types of activity in the construction of scientific knowledge by borrowing from Wittgenstein’s language games theory (Lyotard, 39

1979/1984, p. 10). The first activity is the denotative game of pure or “normal” science, the business of proposing, testing, and applying theory, of establishing systems, rules and procedures, making statements and validating them by demonstrating their compliance with laws and principles accepted to levels of statistical significance. The denotative game as played by the pure science disciplines produces “statements” that place the deliverer, research scientist, in a position of authority with respect to addressees or audiences, including science students who receive the “referents” of scientific statements in authoritative form in textbooks and scientific journals (Lyotard, 1984, p. 9). The second type of activity requires critical reflection on the values and aims of the first activity, although in the modern era these two functions often failed to intersect. These two broad activities reflect the complementary approaches, “internal” and “external”, recognised in the social history of science (Hesse, 1980). As Hesse explains, the “internal” approach is concerned with “rationality” and the way scientific enquiry produces and verifies scientific statements arising from observation, proposition, hypothesis testing and experiment. The “external” approach widens the internal perspective by addressing pragmatic factors, social, educational and economic considerations, and the ideology behind the theoretical belief systems of the sciences (Hesse, 1980, p. 29). In the postmodern era, the partition of the internal formation of scientific statements, and the external evaluation of their social utility and economic viability, is unacceptable. The “truth claims” of science are being challenged such that scientific statements require legitimation in both epistemological (knowledge) and socio-political arenas, and the language game of science has become a game of pragmatics (Lyotard, 1979/1984, pp. 23-27). A new distinction has arisen, in addition to internal and external criteria, between the game of science or “truth”, and the game of technology that seeks optimal performance and efficiency, or the “performativity criterion” (p. 41). The concerns of the technical game are informed less by the questions: Is it true? Is it just? Than by the questions: Is it useful? Is it saleable? Is it efficient? (p. 51). The technical game of science is based on power and money that influences and legitimates scientific knowledge and increasingly determines who has the power to be “right” (pp. 41-47). Scientific education for the game of technology increasingly involves training in the languages of computers, cybernetics (information theory), informatics (information management), telematics (communications technologies) and artificial 40 intelligence (AI) (p. 48). A similar performativity criterion applies to education (Lyotard, 1979/1984, pp. 47-53), and the higher education system, in the expectation that it will supply social systems with players capable of fulfilling their roles at pragmatic posts, will be less concerned with ideas than with skills and techniques (p. 48). This issue has become a major concern in higher education research seeking to integrate sometimes competing perspectives of universities, governments, students and employers (Brennan et al., 1999; Gerber, & Lankshear, 2000; Gibbons et al., 1994; Symes, & McIntyre, 2000). The equation between quality and completion time in higher degrees research is a case in point. There is a “new official research economy” in universities that aims for effective research training. The problem with this, as Bernstein (2000) explains, is that it puts competitive pressure on research students to perform more quickly and with fewer resources. This inevitably affects how research is conducted, data are collected and analysed, and the way reports are shaped, and there is a danger that theoretical innovation will be stifled and that methods will be left unchallenged and undisturbed (pp. 131-132). Whereas this problem is not within the scope of this thesis, it does impinge on general discussions about quality in higher education. This is because there is now a requirement for higher education courses to adopt transdisciplinary perspectives and consider the social and economic factors that impact on the validity of disciplinary knowledge. In recognition of the need to rethink university education in the light of transformations in the way knowledge is produced, a group of international researchers, science policy analysts, educators, and sociologists came together in the 1990s to explore the problem (Gibbons, et al., 1994). Lyotard’s language games distinctions, denotative, pragmatic and technical, can be redrawn in terms of the distinctions Gibbons et al. (1994) refer to as Mode 1 disciplinary and Mode 2 trans- disciplinary knowledge systems. Mode 1 knowledge is produced in the “disciplinary primarily cognitive context” of the university (p. 1). Mode 1 knowledge, “identical with what is meant by science”, is produced by disciplines such as physics, chemistry, geology and biology, which are governed by internal rules and procedures, and have not been, in the past, greatly dependent on outside influences (p. 3). Mode 2 knowledge systems have arisen because disciplinary Mode 1 knowledge systems were failing to tackle adequately, the complex problems arising in the nexus of science, technology, environment and society (p. 3). Mode 2 knowledge is transdisciplinary, reflexive and “socially distributed” because it is 41 produced by groups of experts from different disciplines coming together transiently to solve particular problems, by taking political, economic and social factors into account in addition to scientific and technical knowledge (Gibbons et al., 1994, p. 3). The biotechnology industry is exemplary of a Mode 2 knowledge system because it involves collaboration between science disciplines such as physics, chemistry and molecular biology, technology and non-science fields engaged in business, politics and legal activities, and is subjected to public scrutiny (as discussed in Sections 2.2 & 2.3). Higher education must now address more than disciplinary knowledge, because wider skills are needed for Mode 2 knowledge (Gibbons, 1999). In his projections for education in the postmodern era, Lyotard (1979/1984) anticipated that there would be a vast market for competence in operational skills, and also for competence in problem posing and innovation. It would also be necessary to make connections between fields traditionally separated, by including “interdisciplinary studies” in courses (p. 52). Gibbons et al. (1994) argue similarly, that there is a need for skills in problem posing in addition to problem solving, but suggest that it is not the interdisciplinary synthesis of ideas that is needed, but “transdisciplinary studies” that bring different knowledge sources together in specific ways for specific problems at sites of application on specific occasions (p. 27). As a result of these activities, the pure/applied research and university/industry knowledge distinctions associated with Mode 1 disciplinary knowledge become less relevant (p. 30). In more recent investigations, the distinctions between pure and applied science, and universities and workplaces as sites of learning, have been more directly challenged (Boud, 1998; Boud, & Solomon, 2001; Symes, & McIntyre, 2000), along with the modern university distinction between liberal and vocational conceptions of education (Symes, 2000). The liberal vocational distinction is of particular interest in this thesis because work in the medical sciences has traditionally been conceived as a professional vocation (AIMS, n. d.). CBS-MS (1993) give no indication that medical science can be understood along the lines of a transdisciplinary Mode 2 knowledge system (discussed in Section 3.4.3). Medical science courses tend rather to be conceived along the lines Schön (1983) describes for many professions, according to the model of technical rationality that privileges pure science knowledge over professional knowledge. This is illustrated in the way the subjects are ordered, by placing scientific subject matter at the beginning of courses, followed by applied 42 clinical subject matter and practical courses (see QUT Handbook, n. d. for Bachelor Applied Science [Medical Science]). Medical laboratory science can be characterised further as a Mode 2 knowledge system, in the light of Foucault’s description of the transformations occurring in clinical medicine in France at the turn of the nineteenth century (Foucault, 1969/1972, 1963/1973). Foucault’s characterisation of the medical clinic (1963/1973) bears many common features with Mode 2 knowledge systems, and is applicable to the medical laboratory sciences. Foucault (1969/1972), applying the term “discursive formation”, considers that clinical medicine has an “architectonic” basis in that it draws on disciplinary scientific knowledge codified in statements, rules, principles and practices (p. 5). It is also “discursive” because it accounts for social, political, institutional, economic and other widely dispersed factors (p. 31). Clinical medicine is thus a prime example of Foucault’s theoretical characterisation of scientific discourses as “discursive formations” (Foucault, 1969/1972, pp. 31-34). Foucault’s classification of the medical clinic is addressed in this section because it is applied to the structure of medical science Discourse or Mode 2 knowledge from the clinical chemistry perspective in Section 6.2. Throughout this thesis transdisciplinary Mode 2 knowledge systems, pragmatic and technical language games, and discursive formations are roughly equated, and the pure and applied science distinction is considered to be inappropriate in the medical sciences. The terms Mode 1 and Mode 2 knowledge systems are adopted because they are useful for distinguishing between pure disciplinary knowledge produced in research universities and transdisciplinary knowledge dispersed throughout many knowledge domains and sites of application. This terminology is applicable as long as it is understood that the distinction between Mode 1 and Mode 2 knowledge is an illusion. There is no dichotomy, they coexist on a continuum, and universities have always been concerned with the production and transmission of both forms of knowledge (Symes, & McIntrye, 2000; Usher, 2000). As Usher (2000) explains, the term “mode” is applied to knowledge to indicate that different kinds of knowledge are produced through different channels, and different kinds of social practices (p. 102). Latour (1987) demonstrates in his ethnography of science in action, that the final presentation of new scientific knowledge masks the disputes, power struggles, financial constraints, limitations of resources, and other pragmatic factors embroiled in the quest for new knowledge. Mode 1 and Mode 2 are nonetheless useful for distinguishing between internal “architectonic” scientific 43 activities, observation, hypothesis testing and experiment, and the external “discursive” and pragmatic considerations imposed from without. In this thesis the term Mode 2 knowledge is applied to the medical sciences in general, and to clinical chemistry and the other medical science disciplines, because scientific and pragmatic factors must be considered in evaluating tests in EBLM. There will be a problem with the classification of medical science as Mode 2 knowledge, if medical science education follows the model of “technical rationality” applied in many forms of professional education, as brought to wide attention by Schön in the 1980s (Schön, 1983, 1987). In the aftermath of the social upheavals of the 1960s and 1970s, Schön (1983, p. viii) responded to criticisms of professional knowledge in major (architecture, medicine, law) and minor professions (applied sciences, allied health sciences). Claims were made for example, that many professionals were not stretching themselves beyond the comfort zones of their socially legitimated knowledge (p. 5) (e.g. Illich, 1971). Although Schön (1987) focused specifically on architecture education in North America, his observations are relevant to most professions (Kemmis, & McTaggart, 2000; Schön, 1991), especially those engaged in high levels of technical work, at least if they follow a “technically rational” model of education. The model of technical rationality, Schön (1987) argues, arose out of a complex set of issues arising with the research university in the nineteenth century. Certain forms of “objective” so-called “positivist” forms of scientific knowledge are privileged and the emphasis is placed on “instrumental problem solving” grounded in systematic, preferably scientific knowledge” (p. 8). In the quest for social legitimation, professions struck a bargain with research universities early in the twentieth century, such that professional schools would receive social legitimation if they followed a “normative curriculum” (p. 8). A normative curriculum demonstrates a “hierarchy of knowledge”, in the order of basic sciences, applied sciences and their technical applications in everyday practice, and privileges pure science knowledge over practical knowledge (p. 9). In professional education, attempts are made to mould professional problem solving situations into scientific research models, based on assumptions that “academic research yields useful professional knowledge”, and prepares professionals for “real world practice” (p. 10). If medical science education follows a technically rational model of professional education (implied but not determined), it is incomplete. There are three 44 points of concern raised by Schön (1983, 1987) with respect to professional education that have currency for medical science education at the turn of the twenty- first century. Firstly, there is a gap between professional knowledge and real world practice because unexpected problematic situations arise in professional practice requiring creativity and artistry on the part of professionals (Schön, 1987, p. 11). There is a body of professional knowledge and rules to link knowledge in practice, on which basis computerised diagnostic Expert Systems can function (p. 34) (see also Abbott, 1988; Gillies, 1996; Sikaris, 2001). This body of knowledge and rules cannot however account for artistry, the “framing of problems”, the invention of “on- the-spot” experiments and “reflection-in-action” that creative professionals use to deal with unexpected situations in everyday practice (Schön, 1987, p. 35). The term “troubleshooting” is central to this kind of problem solving activity in the laboratory (Barley, & Orr, 1997, p. 199), but there is a need to make distinctions between what human experts and Expert Systems can do (e.g. Bedard, & Chi, 1992). The second problem raised by Schön (1983) is that the problems of real world practice are highly complex, and it is doubtful that professional knowledge is adequate to deal with complex social, environmental and other issues that socially legitimated professional knowledge helps to create (p. 13). As Schön argues, these complex issues require attention from the professions using critical self-reflection, and not just by “radical critique” imposed from outside the professions (p. 290). In the medical sciences which are caught up in a healthcare system which is a tangled web of competing and conflicting values, problems will require more than technical solutions. The results produced by molecular diagnostic investigations of infectious diseases, or cytogenetic, chromosomal investigations of genetic abnormalities, are cases in point. Interpretations require more than disciplinary knowledge because there are also social and ethical problems to consider, for which there are no clear cut solutions (AACB, 1999c, 2002b, 2002c). The same will be true for laboratory test evaluations in EBLM (Muir-Gray, 1997; Price, 2001). The third problem, as Schön (1987) proposes, is that the power of technical rationality is growing, as basic biomedical research makes “increasingly strident claims” about what medical science can achieve (p. 315) (for examples of hubris in the medical sciences, see Moynihan, 1998, & Lewontin, 1993). As a result of the high financial stakes and the rush to make new discoveries, Schön (1987) argues there is a danger that professional schools will have a reduced “disposition to educate 45 students for artistry and practice”, and an increased “disposition to train them as technicians” (p. 315). Schön’s observations find indirect support in certain commissioned reports that investigated higher education in Australia in the 1990s (Aulich, 1990; B/HERT, 1992; NBEET, 1992; OECD, 1997, 2000). One report concluded that “Australia is producing highly trained technicians who are undereducated in the broad sense of the term” (Aulich, 1990, p. xiii). It was concluded that so-called general or liberal studies were needed in addition to technical training. Because many professionals are reflective practitioners despite their technically rational training, Schön (1987) focuses attention on what competent professionals do at work, claiming that better bridges are needed between workplaces and universities (p. 12). More recent investigations into workplace learning explore the potential for eliminating dichotomies such as theory and practice, pure and applied science, university and workplace learning (Boud, 1998; Boud, & Solomon, 2001; Symes, & McIntyre, 2000). A similar ethos is applied in discussions about the literacies and competencies needed by “smart workers” to keep industries globally competitive and viable, that also confer dignity on workers (Lankshear, 2000, p. 117). There is potential in the conception of liberal-vocational professional courses expanded for transdisciplinary (Mode 2) knowledge, that can satisfy all parties concerned, workers, employers, governments and communities (Symes, & McIntyre, 2000). This concept represents a challenge for universities (Gibbons, 1999), and its significance is investigated further in the next section in ideas about the purposes of universities. This discussion is important with respect to medical science education, because medical scientists are being displaced by automation, robotics and informatics (Section 2.3). Other avenues of employment are needed, or higher level skills in symbolic analysis for fewer highly skilled positions available in laboratories, and for participation in EBLM. In each case new skills are needed.

3.3 Kinds of university for liberal and vocational education

In Australia, debate about the purpose of the university was reinvigorated in response to the “Dawkins” structural reforms in the late 1980s which created a unified national system of higher education expected to be economically viable and to increase the participation of diverse groups of people (Dawkins, 1987, 1988). 46

These reforms provoked heated discussions which led to formal investigations into the quality of higher education (Aulich, 1990; B/HERT, 1992; NBEET, 1992; OECD, 1997, 2000). Negative responses to the “Dawkins” reforms were couched in the rhetoric of liberal values such as learning in the pursuit of knowledge for its own sake, and academic freedom (see Coady, & Miller, 1993; Coaldrake, & Stedman, 1998; Gibbs, 1989). Other responses demonstrated the complexity of the situation, not being reducible to simple distinctions between instrumental economic benefits and the intrinsic cultural values of education (Hunter, Meredyth, Smith, & Stokes, 1991). The distinction between liberal and vocational education is a false distinction if education is properly conceived, broadly as preparation for life and a career, and not just for work in the narrow sense (Lewis, 1994; Symes, & Preston, 1997; Symes, 2000). The sentiments of inquiries investigating higher education in the 1990s are captured in two official reports that attempted to provide a balance between liberal and instrumental values in their visions of higher education for the future. The National Committee of Inquiry into Higher Education in the United Kingdom (NCIHE) expressed that higher education aims to sustain a learning society, to promote the pursuit of knowledge for its own sake, and for the economic and social benefits it brings in shaping a democratic, civilised, inclusive society (aims and objectives, NCIHE, 1997). The Commonwealth of Australia in conjunction with the OECD, provided a similar vision, tailored specifically for the Australian context (OECD, 1997), emphasising the need to stimulate “a more coherent and liberal undergraduate curriculum in universities” (p. v). Although much of the discussion about the liberal university harks back to the nineteenth century and romantic ideas of the university (Coady, & Miller, 1993; Gibbs, 1989; Hunter et al., 1991; Readings, 1996), the “idea of a university” (Newman, 1852/1959) has strategic moments in the last millennium that demonstrate shifting perspectives. The medieval university was built upon the “order of the disciplines” which were separated according to their nature and matter, so that no “unifying principle” was needed (Readings, 1996, p. 56). There were seven liberal arts or faculties referred to as “the trivium” encompassing grammar, logic, and rhetoric, and “the quadrivium” encompassing arithmetic, music, geometry, and astronomy, which prepared students for studies in law, theology, and medicine (p. 56). The eighteenth century Kantian, German or Enlightenment University was built upon the unifying principle of “pure reason” as a bulwark against religious 47 superstition and blind acceptance of tradition, and different conceptions of the university emerged in the nineteenth century from this tradition (Readings, 1996, p. 55). In Germany, the university was built upon the idea of culture and the Humboldtian guiding principle of education as “Bildung” (edification), the formation of the self and of citizens through their exposure to knowledge and culture. The cultivation of moral character was needed for the purpose of building a German nation state (Readings, 1996, p. 62). The University of Culture, it was expected, would provide a bulwark against the excesses of pure reason, because culture requires a process of aesthetic education which facilitates the move from nature to culture without destroying nature in the process (p. 63). The British idea of a university was entrusted to literary studies and its task of “reflecting on cultural identity” (Readings, 1996, p. 70). Cardinal John Henry Newman’s Idea of a University (1852/1959) emphasised the pursuit of knowledge for its own sake, for the economic good of the community, and for the cultivation of intellectual skills, personal autonomy, and balanced judgements (p. 12). In Newman’s university liberal knowledge is not particular knowledge but “intellectual culture” and philosophy is positioned in opposition to practical knowledge or the “principle of utility”, as Readings puts it, against “the mechanical specter of technology” in the industrial age (1996, p. 75). The British university model, epitomised by Oxford and Cambridge universities, noted for being narrow and elitist, aimed to cultivate “gentlemen” through literary and liberal arts studies, and the pursuit of knowledge for its own sake (Readings, 1996, p. 74) (see also Docker, 1994). In North America the situation was more complex as attempts were made to democratise the university using two broad contradictory principles known as the “Jacksonian and “Jeffersonian” principles (Harvard Committee, 1945, p. 31). The “Jeffersonian” was elite in that it aimed for excellence by providing opportunities for gifted students, whereas the “Jacksonian” aimed to raise the level of average students, and so sought justice, equity, and mass provision. General education was promoted in the American model because it provided a pathway to emancipation for its citizens, and fostered certain “traits of mind” that specialisation could not. In the sciences for example, it was expected that a general education would foster understanding not only of the substance of science, but also of its methods, achievements and limitations, thereby encouraging people to discriminate among 48 values (Harvard Committee, 1945, pp. 64-65). General education it was argued should suffuse all special education because “specialisation can only realise its major purposes within a larger general context” (p. 195). The distinction between general and special education has a counterpart, in the sciences at least, between disciplinary Mode 1 knowledge and transdisciplinary Mode 2 knowledge encompassing wider pragmatic economic and social concerns (Gibbons et al., 1994). The American democratic model also foreshadows the late twentieth century shift in the Western world, towards a more democratic and inclusive higher education system (Coaldrake, & Stedman, 1998; Marginson, 1997). The principles underlying general and liberal education can be conflated for practical purposes, but there are many ways to define liberal education (Symes, & Preston, 1997). If the opposition liberal/vocational education is adopted, it sets up an opposition between education as “preparation of the whole person for life in the broad sense” and education as preparation for “work in the narrow sense”, and the instrumental perspective of education for work is rejected because it corrupts the meaning of education (p. 63). From other perspectives, the liberal/vocational opposition is based on historical inaccuracies (Stokes, 1991; Symes, 2000). As Stokes (1991) argues, European universities have traditionally been concerned with producing graduates with “specific technical skills and practical forms of knowledge” (p. 200). The term “instrumentalism”, having many meanings, requires clarification to release it from its pejorative connections. There is also the need to acknowledge that higher education serves multiple values in its interactions with students, governments and communities (p. 225). Abbott (1988) explains the difference between liberal and vocational education in terms of class distinctions, vocational education being for the professions and the “making of oneself through a career”, versus liberal education for “Bildung” being designed to reinforce one’s “pre-established station in life” (p. 196). There should be no distinction between liberal and vocational education if both approaches are properly conceived (Dewey, 1916; Lewis, 1994; Symes, & McIntyre, 2000). There have been many movements, mainly British and North American, attempting to bridge the vocational/liberal divide, which have their roots in the educational philosophies such as those of John Dewey (1916), A. N. Whitehead (1929), and Mary Warnock (1977) (see Lewis, 1994). Despite their different emphases, these philosophers collectively aim to prepare students for the world of 49 work using a curriculum that is both useful and liberally conceived. As Penington (1994) explains, there is no inherent limitation in the idea of vocation which is time- honoured with respect to the church, law, and medicine. The distinction between liberal and vocational education is based on unproven assumptions about how learning proceeds and perceptions of whether habits or understanding are needed. From Whitehead’s (1929) perspective, the liberal/vocational dichotomy is fallacious because “there can be no adequate technical education which is not liberal, and no liberal education which is not technical”, both technique and intellectual vision are needed (p. 74). Professional education that is overly reliant on technical rationality as Schön describes (1983), can thus be understood as “false vocationalism” because “true vocationalism” is democratic and aims to equip students with general skills that enhance their life choices, and their social contribution (Lewis, 1994; Silver, & Brennan, 1988; Symes, 2000). A democratic approach to education will consider the needs of students because the needs of industry are constantly changing (Lewis, 1994). This view describes the worker empowerment premise on which emancipatory literacy is based (Freire, 1972; Gee, 1996; Lankshear, 2000). There are new problems to consider in the more recent manifestations of the Western university, the excellent university (Coaldrake, & Stedman, 1998; Readings, 1996) and the “borderless”, “on-line”, “e-university” (Beckett, 2000; Cunningham et al., 1997; Laurillard, 2002). Neither liberal nor vocational ideas of the university are adequate to explain its function at the turn of the twenty-first century. In the “techno- bureaucratic” university, intellectual activity and culture are overshadowed by the pursuit of excellence (Readings, 1996, p. 55) and performativity (Lyotard, 1979/1984). The excellent university must be effective, efficient and flexible and satisfy the needs of competing groups, governments, employers, students and communities. As Coaldrake and Stedman (1998) explain, the social, political, and managerial changes in universities in the late 1980s were necessary in order to ensure that universities were contributing to the broad national economic agenda (p. 19). The higher education sector is now implicated in economic policy formation, in strategies for population management, and in the preparation of labour for work, and also in retraining programs for the unemployed (Marginson, 1997). The excellent university is complicated further by the possibilities of “borderless education” and on-line learning in the “e-university” (Beckett, 2000; Cunningham, et al., 1997; Gerber, & Lankshear, 2000; Laurillard, 2002). Questions 50 are raised about the competencies and literacies needed in computerised environments, and what distinctions if any, can be drawn between visual literacy as demanded by film, video and television media, and print literacy as demanded by written media (Beckett, 2000; Kress, & Van Leeuwen, 1996). Competencies and literacies are also considered in terms of the requirements of the “new work order” under global capitalism, and the prospects of workers, displaced by robots and computers in “high-tech” workplaces. There are additional pressures for flexibility and teamwork, and technological and information literacy, over and above the requirements of disciplinary knowledge (Aronowitz, & DiFazio, 1994; Gee, Hull, & Lankshear, 1996; Gerber, & Lankshear, 2000). The jury is still out on the e-university (Laurillard, 2002), but there is no reason why the performance and efficiency criteria of the bureaucratic excellent university cannot be convergent with the emancipatory, progressive criteria of the democratic university that focuses on student participation and the quality of learning. In a “knowledge economy”, knowledge has use-value, it can be put to work, and this need not debase the values of education (Symes, & McIntyre, 2000, p. 2). The OECD (1997) emphasises the benefits of vocational training and a “real- world” emphasis in higher education because it prepares graduates for the future. It equally stresses that vocational education and training programs need to be infused with general or liberal studies, so that instrumentalism in the narrow sense, understood as technical rationality in the professions, is guarded against. The rest of this chapter explores competence, literacy, and the possibilities of converging liberal and vocational ideas about learning, with a view to reconciling the productivity and efficiency demands placed on workers by employers, and the knowledge work that can also emancipate workers (Gee, 1996; Lankshear, 2000). These discussions are first placed in the context of education reforms and strategies already in place, or at least on the agenda in professions such as the medical sciences.

3.4 Educational reforms and strategies

The most significant educational reform in professional medical science education is perhaps Competency-Based Standards for Medical Scientists (CBS-MS, 1993). This section reviews CBS-MS and explores the general debates about the usefulness of competency standards for the professions. Competency is juxtaposed 51 with other attributes, in particular capability and professional expertise, and placed in the context of the workplace and the community of professional medical science practitioners. This is because many useful explorations of professional education aim to consider what expert professionals actually do at work, in addition to considering the formal knowledge on which professional expertise is based (Boud, 1998; Boud, & Walker, 1998; Boud, & Solomon, 2001; Schön, 1983, 1987; Symes, & McIntyre, 2000). Workplace learning or Work-Based Learning (WBL) provides a promise and a challenge, particularly for technical professions in which work is conducted using expensive automated computerised equipment. Educational policy is not usually directed towards professions because it is expected that the professions can sort out what is needed for conceptual understanding and practical experience in the cultivation of competencies called for by employers (Bowden, & Masters, 1993). The move towards competency-based-standards by allied health professions, including the medical sciences in Australia in the early 1990s, is illuminated by its comparison with the British capability movement in the next section.

3.4.1 Competence and capability

In the United Kingdom (UK), the Education for Capability Manifesto was issued in 1979 by the Royal Society for Encouragement of Arts, Manufactures and Commerce in response to “frustration with the artificiality of the divide between ‘education’ and ‘training’” (Stephenson, & Weil, 1992, p. xiii). An imbalance was perceived between the acquisition of knowledge and its use, with the emphasis being placed on doing, whereas people who “can do” and “know about” were needed (p. xiii). It was perceived by a UK advisory committee on science and technology that higher education courses were becoming less popular because they provided students with “an unsatisfactory intellectual and educational experience and an inadequate preparation for future jobs” (Weil, & Emanuel, 1992, p. 127). In mathematics and the sciences it was believed that the cause of dissatisfaction was the accumulation of factual content which forced students to excessively rote learn and to insufficiently understand fundamental principles. The broad aim of the learning for capability movement was to facilitate a shift from teacher-dominated approaches which “prepare inactive and passive learners for predictable situations” towards student centred approaches which aim to encourage students to be responsible and 52 accountable and to prepare them for change (Stephenson & Weil, 1992, p. 4). Capability is not just about skills and knowledge, it involves “taking effective and appropriate actions within unfamiliar and changing circumstances” that require “judgements, values, the self-confidence to take risks, and a commitment to learn from experience” (p. 2). Because these are the same capabilities needed in the workforce, the seeds of WBL were sown. WBL projects are expected to involve employers as collaborators and to bring students together in groups to solve particular problems, stimulating active inquiry skills and creativity as students encounter unfamiliar problems. WBL activities include learning contracts, self and peer assessment, case studies worked on in teams, the production of written and oral reports, and work placements to provide a context for learning (Weil, & Emanuel, 1992, pp. 129-137). The capability learning movement has a sound basis in educational principles along similar lines to those espoused by John Dewey in his theory of experiential learning which entails not just doing but undergoing and understanding what has been undergone (Dewey, 1916) (see also Barnett, 1994). In contrast, the movement towards employment related competencies in Australia in the early 1990s attracted criticism by those who perceived that the competency agenda was driven by economic rationalist concerns about performance in the world of work, rather than by a genuine interest in the quality of students’ learning (Marginson, 1993; Penington, 1994). A rationalistic view of competency it is argued, provides a stark contrast to the liberal concerns of a genuine higher education which has among its goals the cultivation of higher order thinking and practising skills in students (Barnett, 1994), and also considers workers’ experience (Sandberg, 1994, 2000). The assessment of competence by the National Training Board (NTB) at that time was made by the observation and measurement of work related performances (Marginson, 1993, p. 13). By placing the emphasis on performance outcomes it attracted much heated debate and criticism. It was accused of limiting definitions of competence to the terms of behaviourist psychology by providing only an “operational definition” of competence for the purpose of testing and measurement (Norris, 1991, p. 332). Meredyth (1997) explains however that there are “important ground level features” missing in this type of debate that shifts the expectations of competency to an inappropriately higher plane. “Policy statements, programmatic objectives, and administrative routines cannot be treated as if they were theoretical reflections of the 53 kind conducted within the academy” (p. 607). There is a difference between “bureaucratic reasoning” and “principled reflection” and the way that “states address themselves to citizens cannot be summed up in such abstract and ideal terms” (p. 608). The quality of learning imperative was after all driving the competency reforms as a response to the perceived inadequacies of higher learning by employers. Industry complaints about workers provided the impetus for the development of “key competencies” in secondary education (Mayer, 1992). Complaints were also directed at the higher education sector as employers claimed that university graduates were unable to extrapolate their knowledge, make the appropriate connections, adapt, and apply that knowledge in the world of work. Several reports in Australia triggered by a senate inquiry into higher education (Aulich, 1990) addressed this issue (B/HERT, 1992; Bowden, & Masters, 1993; Candy, Crebert, & O’Leary, 1994; Marginson, 1993; NBEET, 1992). Although these reports represented different opinions across the various sectors from government, industry, and higher education, some common themes emerged. It was generally agreed that the training of discipline specific skills and knowledge base were adequate, but certain capabilities, capacities, attributes and skills seemed to be lacking in graduates. Particular examples are oral and written communication skills, the ability to work in teams, and skills in analysis and problem solving (Bowden, & Masters, 1993, p. 172). The Australian competency reform discussions of the early 1990s, although mainly directed towards the technical training arena, were also addressed by the professions in response to the National Office of Overseas Skills Recognition (NOOSR) agenda begun in 1989 (Bowden, & Masters, 1993; Marginson, 1993). It was NOOSR that introduced a broader idea of competence for the professions, requiring more than the ability to perform specific tasks efficiently. A combination of abilities, skills, and attributes are required for the complexities of professional life, and to meet the standards expected in fields of employment (Marginson, 1993, p. 13). Although the prime goal of NOOSR was to develop a competency-based approach to the assessment and recognition of migrants’ professional skills, it also became the main forum for the development of professional competencies, particularly in many paramedical fields (including medical science) (Marginson, 1993, p. 4). At the same time there was “recognition of prior learning” (RPL) which opened up avenues to university entry to students from overseas and mature-aged students who did not have formal entry qualifications (Candy et al., 1994). The 54

NOOSR agenda for competency addressed to the professions was a significant improvement on the original competency conceptions, but also resorted to the measurement of performance indicators so that the problem of the assessment of professional competence remained. Various commentators and researchers into professional competence argued that there are certain professional skills and attributes that cannot be observed and measured (Bowden, & Masters, 1993; Marginson, 1993; Penington, 1994). Since formulations of competence were first introduced, more complex constructions of competence in workplaces have come to light (Bowden, & Masters, 1993; Gerber, & Lankshear, 2000; Marginson, 1993; Sandberg, 1994, 2000). However the great value of the competency debates in Australia, as in Britain, has been the promotion of discussions about the links between the world of work and higher education (Boud, 1998; Boud, & Solomon, 2001; Bowden, & Masters, 1993; Garrick, & Kirkpatrick, 1998; Hughes, 1998; Marginson, 1993: Reeders, 2000; Symes, & McIntyre, 2000). It has focused attention on the assessment of workplace performance, the way that learning in the workplace is achieved, and how knowledge can be integrated with skills and attitudes in professional practice. The knowledge skills and attitudes basis of workplace competence and performance has more recently been built on by conceptions such as “smart work” and a “new work order” that meets the competitive demands of “post-capitalism” (Gee et al., 1996; Gerber, & Lankshear, 2000). In smart workforces companies remain viable through flexible teamwork and the efforts of smart workers using extended forms of competencies and literacies including critical, technological and information literacies (Lankshear, 2000; Tinkler, Lepani, & Mitchell, 1996). The idea of WBL thus represents a challenge to both workplaces and universities.

3.4.2 The challenge of work-based learning

WBL programs have among their aims an ideal to harness what is valuable, economically and educationally in the learning that takes place consciously or unconsciously in the workplace. The workplace thus becomes a suitable site of learning that appropriately conceived, merits the conferring of university degrees (Boud, 1998; Boud, & Solomon, 2001; Symes, & McIntyre, 2000). In order to ensure such programs are viable and worthy of accreditation for professional degrees, it is 55 necessary to harness the academic rigor already inherent in the work practices of reflective professionals, as Schön (1983, 1987) recommends. Two issues stand out when workplace learning is considered. On the one hand, universities participating in the education and training of professionals will ideally emulate reflective work practices. On the other hand, if the workplace is to be a suitable venue for learning, it will be necessary to ensure that the intellectual or academic components of work are addressed (Boud, 1998; Boud, & Solomon, 2001; Boud, & Symes, 2000; Hughes, 1998; Reeders, 2000; Schön, 1983, 1987). This means that whether professional practice placements are considered, or simply work, it cannot be assumed that learning occurs as a result of simply being in the workplace. The articulation between academic learning and workplace learning requires deliberate effort or “mediation”, as Laurillard suggests (1993, 2002). Early reforms of the competency agenda identified the need to link academic and workplace knowledge, and to identify generic skills and capacities that can be incorporated into the curriculum, and teaching and learning activities (Bowden, & Masters, 1993; Higher Education Council of Australia [HEC], 1992; Marginson, 1993). For example, Bowden and Masters (1993) provided a “relational model” to extend competence beyond earlier limited operational performance conceptions. This model of competence aimed to link discipline-based “knowledge skills and attitudes” with “observable practice”, that is what people do in the workplace, and this would be accomplished through “underlying capacities of a generic kind” (pp. 155-159). Competent professionals would be expected to have the qualities and skills needed in professional life for identifying and approaching problematic situations. This would require in addition to sound disciplinary knowledge, analytical practical problem- solving capacity, good communication skills, and the ability to exercise independent critical thinking. The so-called “transferable skills” movement aimed to develop generic capacities to enable graduates to cope with unexpected and unforeseen situations as well as with specific foreseeable situations (Barnett, 1994, p. 79). In transferring generic skills across different contexts, it was expected that the transition between the university and the world of work would be facilitated, and the same skills required for a successful economy would be those sought by a genuine higher education (p. 80). The generic skills idea was seen as problematic if assumptions were made that transfer between the academy and work would be automatic (Marginson, 1993, 56 pp. 20, 34). Transferability is possible and can be optimised but it cannot be taken for granted because academic skills do not necessarily coincide with work related skills requirements (Marginson, 1993, p. 36). There may be ambiguity of purpose, conflicts of interest and differences in skills-requirements between industry and university sectors. The competence of a worker as “self-directed being” is not necessarily the same as the competence of a worker as “someone else’s employee” (p. 116). There are however, conditions of possibility for skills transfer in various situations and certain “strategic skills” and capacities that are likely to assist such transfer. For example, “learning how to learn, understanding the role of knowledge sets in different contexts, confidence, the capacity for initiative, flexibility” and “lateral thinking” (p. 119). Proponents of the relational model of competence (Bowden, & Masters, 1993) claim it is unlikely that generic capacities such as “problem solving, critical reasoning, planning and organisation skills, interpersonal and communication skills”, will be transferable across disciplines. It is suggested rather that such skills can be meaningfully assessed only in terms of specific bodies of knowledge or practices on application to real life problems (p. 159). From other perspectives, it is also argued that generic skills must be identified at the level of the disciplines, but due to the nature of these skills it will not be necessary to learn them anew each time a new situation is encountered (Clanchy, & Ballard, 1995; McPeck, 1990). From the socio-cultural perspectives associated with the study of language, literacy and culture, the generic skill of critical thinking within a discipline will not be sufficient, and there are multiple literacies and many discourses to consider (Gee, 1996). Disciplines are discourses or knowledge communities with highly specialised ways of thinking acting and doing, and in higher education there is a need for criticism both in and of discourses (Gee, 1996; Gee et al., 1996; Gerber, & Lankshear, 2000; Lemke, 1995). In the light of socio-cultural perspectives, understanding is required of the nature of knowledge, its structure and it comparison with other forms of knowledge (Aulich, 1990; Barnett, 1994; Donald, 1999; Gee, 1996; Laurillard, 1993, 2002; Marginson, 1993; Matthews, 1998; OECD, 1997, 2000). Crossdisciplinary courses provide the generic academic capacity of broadened social and cultural outlook because they expose students to the limits of specialist knowledge, and provide them with insights into other ways of seeing a problem (Barnett, 1994; Marginson, 1993). The comparison of disciplines also gives insights into their philosophy and methods. 57

As Matthews (1998) points out, science education programs that are liberally conceived, include discussions of the nature of science, “its history, methodology, philosophy, social and cultural impacts, and its relation to other forms of knowledge” (p. 996). There is still however a problem to resolve about the viability of liberally- conceived vocational science courses, and whether the time taken to incorporate philosophical and alternative perspectives detracts from the main functions and goals of disciplinary perspectives (Coaldrake, & Steadman, 1998, p. 43). The point remains however that vocational education in the full sense of the term is liberal and the reverse is also true, a true liberal education is vocational (Lewis, 1994; Symes, 2000; Whitehead, 1929). The meaning of these distinctions can be better explained with concrete examples that draw comparisons between the competency expectations of an applied science profession and a humanities discipline.

3.4.3 Competency-based standards for medical scientists

In Australian medical science education there has always been a close link between the higher education sector and professional bodies, for example, the Australian Institute of Medical Scientists (AIMS), the Australasian Association of Clinical Biochemists (AACB), and the Australasian Society for Microbiology (ASM). The role of work placements and professional practice in medical science courses is guided formally or informally by the professional and ethical standards of these professions. The competency standards for medical scientists (CBS-MS, 1993), set under aegis of NOOSR in the early 1990s, were not intended as edict or decree, or as guidelines for the competencies needed by senior scientists. They were intended as guidelines for training and skills formation, as pieces in a “wider reform jigsaw” that included award restructuring and enterprise bargaining (p. 6). It was expected that the translation of competencies into medical science curricula would remain in the province of academia, but they were also expected to be useful for goal orienting professional practice and continuing professional education (CPE) (p. 7). Competency standards for medical scientists present an ongoing dilemma for universities, industries and the professions. Farrance (2000) places competency standards and on-going competency assessments on a list of prominent issues within the politics of pathology. The issue of competency has been further compounded since the inception of CBS-MS by advances in communications technologies, 58 automation and computing, and the effects of global capitalism on laboratory management (as discussed in Section 2.3). A comparative analysis of medical science courses in Australia would be needed to assess how CBS-MS (1993) were received by universities, and to what degree they were implemented in the professional practice components of courses or work experience placements of students in the pathology industry. One Australian study (Martin, 1997) provides a comparative analysis of the effectiveness of different work-based models of university education across different courses. This study incorporates the experiences of one university participating in medical science training using CBS-MS as a guide in the assessment of students’ professional practice performance. The definition of competency in the CBS-MS (1993) manual is that provided by the National Training Board at that time, “the ability to perform the activities within an occupation or function to the standard expected in employment” (CBS-MS, 1993, p. 5). The competencies are categorised as core, general and task-specific. The core competencies are equated with “literacy, numeracy, reliability, communication skills and ability to work in teams”, but these are assumed to be already present (p. 5). The underlying general competencies are those that all healthcare professionals are expected to acquire, particularly legal and technical “generic health professional principles” which put patient welfare above all else; the ability to interact with other health professionals; clarity in communications; and commitment to ethical principles and occupational health and safety issues (p. 11). Task-specific competencies applied to the disciplines (e.g. haematology, biochemistry, microbiology as explained in Section 2.2) provide the main focus of the CBS-MS (1993), and their evaluation is reliant on eight units of competency. The term “unit” is used to describe “a broad area of professional performance” and the term “element” is applied to the components into which these units are divided. The expression “performance criteria” is used to specify “the types of performance in the workplace that would constitute adequate evidence of personal competence”. The term “range indicator” describes more precisely the circumstances in which these criteria are applied. Finally, certain “cues” are selected as concrete examples that illustrate whether a competency has been achieved (p. 8). For example, Unit 1 states “prepare and analyse biological material” (p. 12); Unit 2 states “correlate, validate and interpret results of investigations using clinical information” (p. 16); Unit 3 states “report and issue laboratory results” (p. 18); Unit 4 states “maintain 59 documentation, equipment and stock” (p. 21); Unit 5 states: “maintain and promote safe working practices” (p. 23). Units 6-10 are more broadly conceived, requiring liaison with health workers to improve service, participation in education and training of others, and in CPE, research and development, professional development and accountability. This process thus builds a picture of a medical scientist as one who provides “information based on investigation of biological material” to assist in “the diagnosis, monitoring and prevention of disease” (CBS-MS, 1993, p. 1). It is not in dispute that such competencies are important to laboratory operations and patient welfare. In the study referred to (Martin, 1997), interview data revealed that although workplace supervisors and academics believed the competency standards were less than ideal, they did serve a useful function, given that supervisors had no other guidelines to follow (pp. 52-53). CBS-MS (1993) have also been useful in bringing the industry, profession and universities together. CBS- MS accounts for the interests of work, and CPE is also considered, but the competencies targeted may not be sufficient to satisfy the requirements of workers as lifelong learners when socio-cultural perspectives are considered (Candy et al., 1994; Gee, 1996; Gerber, & Lankshear, 2000). A comparison is drawn between CBS-MS (1993) and the competency requirements of a humanities discipline (as provided by Marginson, 1993), to demonstrate the vocational-liberal distinction. In the humanities discipline, students are expected to be able to ask questions appropriate to their concerns, and to research those questions in a discriminating and critical fashion. They are expected to argue an independent case that draws on an array of material; to demonstrate they can synthesise a wide range of material; and argue their case both orally and in writing; and to be able to work in group situations which reveal that they can respect others views but also argue against them (Marginson, 1993, p. 103). These kinds of competencies are not represented in CBS-MS, although they may be addressed in individual medical sciences courses. If so-called liberal concerns are considered, a scientific curriculum will expose students to the principles of scientific research and method (Matthews, 1998); students will be required to pose questions and research them in a critical fashion; students will be given opportunities to synthesise a range of material through systematic review of existing research, and to identify problems for further research in the process; students will be given the opportunity to communicate orally and in writing; students will have opportunities to 60 collaborate in groups other than in practical situations constrained by logistical factors such as laboratory resources, equipment and demonstrators. The goals in natural sciences and humanities disciplines were compared in a review of studies exploring the links between knowledge and learning (Donald, 1999). It was noted that in the physical sciences, there was a tendency to focus on factual knowledge, as compared to the social sciences and humanities disciplines which tended to focus on understanding, creativity and communication (p. 46). Questions were raised about the size of the knowledge bases in the sciences, and whether they were too large for students to develop liberal humanities style intellectual skills. However the humanities knowledge base is equally large and ever expanding. A more significant point to consider is that there might be different conceptions of the nature of learning in each sector, the sciences and the humanities, for example knowledge as acquisition versus knowledge for intellectual development respectively (p. 46). Literacy theory provides insights into acquisition and learning, and guidelines for advancing competencies in the medical sciences beyond the basic operational requirements of industry. This topic is explored in the next section. In conclusion to this section, since the inception of CBS-MS in the early 1990s, the changes in industry due to economic globalisation, and advances in information and communications technologies, indicate that more complex views of competence are needed in the medical sciences. The relational model of competence (Bowden, & Masters, 1993) aimed to bridge the gap between academic and workplace learning, by placing disciplinary knowledge within the context of real world applications to orient learning. In this model the experience of learners and the context of learning, are addressed, but not the language, literacy and culture in which these activities are expressed. To advance notions of competence in the medical sciences there are many factors to consider: theories of language and literacy to guide competence in discourses (Gee, 1996; Gerber, & Lankshear, 2000); the context of learning, its situation with the activities and tools of the culture to which learning is addressed (Brown, Collins, & Duguid, 1989; Lave, 1988; Lave, & Wenger, 1991); computer- assisted learning (CAL) (Beckett, 2000; Lankshear, 2000; Laurillard, 2002; Tinkler et al., 1996); and value adding work competencies that help keep industries competitive and viable (Drucker, 1993; Gee et al., 1996; Gerber, & Lankshear, 2000; Reich, 1992). An expanded view of competence also seeks to empower workers and 61 students to question the social values that discourses express. By widening their horizons, this can enhance their life choices (Freire, 1972; Freire, & Macedo, 1987; Gee, 1996; Lankshear, 2000). A liberal-vocational approach to knowledge work is required in the medical sciences, as Symes and McIntyre (2000) recommend in general.

3.5 Discourse competence, knowledge work and symbolic analysis

There are new foundations for competence when it is understood as a collective phenomenon shared by those participating in a culture (Sandberg, 1994, 2000). A socio-cultural theory of language and literacy expands on traditional views of literacy understood as facility with the grammar of language, and centres on the notion of “Discourse” with a capital “D” (Gee, 1996), taking the point of departure from theorists such as Pierre Bourdieu (1979/1984) and Michel Foucault (1969/1972). “Discourses are ways of being in the world, or forms of life which integrate words, acts, values, beliefs, attitudes and social identities, as well as gestures, glances, body positions and clothes” (Gee, 1996, p. 127). A Discourse is “a socially accepted association among ways of using language, other symbolic expressions and ‘artifacts’ of thinking, feeling, believing, valuing and acting” that make individuals members of a social group (p. 131). In socio-cultural approaches, a distinction is drawn between the “primary discourses” people acquire through their associations in families, and “secondary Discourses” acquired through interactions in peer groups, at work, and in institutions such as churches, schools and universities (p. 137). There are thus, many ways of being in the world and multiple literacies, and fields targeted in higher education, law and the medical sciences for example, can be understood as secondary Discourses. Understanding is sought as to how people come by the many secondary Discourses they encounter in life, and the many literacies and competencies needed. Literacy understood from the perspective of specific Discourses is defined therefore as “mastery of a secondary Discourse” (p. 143). Gee (1996) makes a distinction between “discourse” (little d) and “Discourse” (Big D) in terms of knowing stretches of language or grammar (operational competence) versus understanding the way the language gets used by a community (cultural competence) (see also Lankshear, 2000, p.104). To be in a Discourse is to be recognised as a member of the community and also to be subjected to its many constraints (Gee, 62

1996, p. 132). An extra critical dimension is added to these criteria because socio- cultural approaches to literacy aim to empower students to challenge the customs, and conventions of existing Discourses (Gee, 1996; Lankshear, 2000). Erickson (1998) applies the distinction to scientific Discourses so that “d” discourse reflects “the conduct of immediate social interaction” and “D” Discourse encompasses the wider social context in which discourses are embedded. Applied to science education, “d” learning requires effective participation in group activities using specific languages, rules, conventions, and patterns of activity, all subsumed within the values of the group. Big “D” learning requires participation in a much larger conversation. In the physical sciences for example, this means engaging not just in the conversations raised by Newton, Einstein and Heisenberg, but also in those raised by their financial patrons (Erickson, 1998, p. 1157). Because scientific knowledge and skills at the “D” level are embedded in value conflicts, “D” science learning requires awareness about issues such as “power, risk, trust, legitimacy and in-group/out-group distinction and ranking” (p. 1158) (see also Harding, 1986; Latour, 1987; Lemke, 1995). The “d” and “D” Discourse distinction thus approximates the distinction drawn in Section 3.2, between Mode 1 disciplinary and Mode 2 transdisciplinary knowledge systems, the latter term being applicable in the biomedical sciences to biotechnology and EBLM. Knowledge communities such as scientific disciplines can be understood as specific cultures or Discourses which base their activities on shared understandings and group interactions conducted using specialised forms of language (Cobern, 1991; Cobern, & Aikenhead, 1998; Gee, 1996; Halliday, & Martin, 1993; Lemke, 1990; 1995, 1998a, 2000). Science education in this view becomes a process of enculturation into the norms, conventions and values of a culture and how it makes sense of the world. Socio-cultural research into science learning will consider that students might hold alternate world views to those held in common by the cultures of science (Cobern, 1991). In other words their formative primary Discourses might be in tension or value conflict with the secondary Discourses of science (Gee, 1996). In order to enculturate students into the ways of science, it is important to consider the characteristics held in common by people with a “scientifically compatible world view” (Cobern, 1991, p. 72). The scientifically compatible world view is one that allows scientists from very diverse cultures to value and successfully participate in the enterprises of science (p. 100). The pure and applied sciences are, however, 63 composed of many Discourses each adopting widely differing pathways of inquiry. As Schwab (1962) explains, physics tends to search for overarching principles to explain the behaviour of matter and the universe, sciences such as biology, physiology and chemistry build classificatory schemas to explain the diversity of life and matter, and pure mathematics operates with self-contained deductive abstract systems using numbers and other symbols. Each scientific Discourse has a specialised way of speaking, acting, describing, depicting, and communicating its processes of observation, discovery, experimentation and verification. They do this with a particular value orientation and with the aid of specialised semiotic or symbol systems (Halliday, & Martin, 1993; Lemke, 1998a, 2000). Analysts of work characterise work in scientific computerised environments as knowledge work and symbolic analysis (Aronowitz, & DiFazio, 1994; Drucker, 1993; Gee et. al, 1996; Gibbons et al., 1994; Lyotard, 1984; Reich, 1992). As Lankshear explains (2000), “the contemporary world is semiotic - a world of signs and symbols”; symbolic analysts add value at work by reconfiguring knowledge existing in abstract symbolic form in novel and creative ways (p. 109). Symbolic analysis is the key to academic knowledge and its mediation will be accomplished through deliberate reflection on the abstract representations used in the scientific workplace, including verbal language, mathematical or chemical symbols, graphs, diagrams and other images (Laurillard, 1993, p. 27). Science literacy requires the acquisition of a body of knowledge, principles, laws and theories, but science is also a “semiotic system” and specific processes are used to acquire and refine scientific information (Halliday, & Martin, 1993, p. 16). Science has evolved a specialised language used to interpret the world in its own terms and not in common sense terms (p. 200). As Lemke (2000) explains, science education is a process of enculturation into a multi-faceted world, and students must be empowered to use all of the languages of science. There are “multimedia literacy demands of the scientific curriculum”, and the technical languages of science incorporate many “modalities of representation” including verbal language, mathematics, graphs, diagrams, and other pictorial means (p. 247). Science learning requires understanding of scientific concepts, and also coordination of the technical languages of science. It requires an ability to fluently juggle its representational modalities, to apply them appropriately, and to freely translate back and forth among them (p. 248). Scientific literacy can thus mean two different things, “a familiarity with basic scientific facts and 64 concepts”, and “the ability to use the complex representational apparatus of scientific reasoning, calculation, and practice” (Lemke, 2000, p. 247). To be educated in science requires multiple literacies or multi-literacies, because its concepts are articulated across multiple media of representation. Multi-literacies are the basis of knowledge work and symbolic analysis in the sciences. From a socio-cultural perspective, to cultivate literacy and competence in the sciences, it is important to mediate between academic and workplace learning by targeting specifically what expert symbolic analysts actually do (Laurillard, 1993, 2002). There are cognitive requirements of experts to juggle symbol systems effectively (Bedard, & Chi, 1992; Chi et al., 1988), but scientific literacy is compounded when competence in a science discipline is understood in terms of Discourse theory (Gee, 1996). Multiple dimensions of literacy, operational, cultural and critical, apply on a continuum between “d” and “D” competence for effective performance in secondary Discourses (Lankshear, 2000), Mode 1 disciplinary, and Mode 2 transdisciplinary scientific knowledge systems (Gibbons et al., 1994). Competence to engage in the immediate requirements for operational activities and rule-governed procedures can be understood as “d” competence, and competence to take issue with the wider implications of those activities, managerial, economic, political, and social, can be understood as “D” competence. The literacies and competencies needed for knowledge work and symbolic analysis at the “D” level in the medical sciences are developed further in the next section.

3.5.1 “D” competence: Operational, cultural and critical literacy

The new literacy studies (Gee, 1996; Lankshear, 2000) expand on traditional notions of literacy understood as abilities with reading and writing at the basic level, and logical skills in scientific reasoning at the advanced level, thought to be a consequence of the Greek alphabet and print (see Eisenstein, 1979; Gee, 1996; Goody, & Watt, 1963; Havelock, 1963; Olson, 1994; Ong, 1988). Research into literacy has revealed there are multiple literacies and alternative modes of reasoning based on experience at sites of learning, and not necessarily acquired through schooling (Brown et al., 1989; Gee, 1996; Lave, 1988; Lave, & Wenger, 1991; Scribner, & Cole, 1981; Vygotsky, 1978). New literacy studies, drawing inspiration from emancipatory literacy theory (Freire, 1972), are also driven by a moral 65 imperative to empower students and liberate them from the constraints of single perspectives (Gee, 1996, p. 36). To encompass the multiple facets of literacy, Lankshear (2000) identifies three broad dimensions and five types of literacy, directed towards secondary Discourses. There are “operational, cultural and critical” dimensions of literacy that bring together “language, meaning and context” (p. 103). Operational literacy is needed to operate effectively within a language system; cultural literacy is needed for understanding the way the language is used for specific cultural purposes; and critical literacy is needed for evaluation, optimisation and revision of existing practices within a given language system (p. 104). In short, the dimensions of literacy entail doing, knowing and understanding, and improving. The five types of literacy addressed to these dimensions of literacy will be adapted in this section for a “D” competence framework that is applicable in the medical sciences. According to Lankshear (2000), the first type of literacy is the old, basic literacy of reading and writing ability or “lingering basics”, assumed to be present at the tertiary education level (p. 111). The second type of literacy is “new basics” associated with critical thinking, logic and modes of reasoning commonly thought of as scientific (p. 111). The third type of literacy is “elite”, cultural, Discourse and discipline specific (p. 111). The fourth type of literacy is second language proficiency, essential for workplaces dependent on global economies and advanced communications technologies (p. 112). The fifth type of literacy includes both technological and information literacies, the ability to handle computers and to manage information effectively (p. 113). In the context of medical science education, the first type of literacy, reading and writing ability, is assumed to be present (CBS-MS, 1993), but the second and third types of literacy warrant specific attention, that is “new basics” and “elite”, Discourse or discipline specific. Because an additional critical dimension applies to elite literacy for socially accountable laboratory medicine, the cultural and critical dimensions will ideally become one in EBLM. The fourth type, second language proficiency, although desirable, has not to date featured as a competency requirement in the pathology industry. The fifth type, combining technological and information literacy which are roughly equivalent in automated computerised (informatised) laboratories, is particularly reliant on print literacy, “new basics” and critical literacy (Lankshear, 2000, p. 105) (see also Beckett, 2000; Laurillard, 2002). It is noted that information literacy requires more than information collection, management and 66 analysis skills, it also requires “systems thinking” and reflection to distinguish “signal” and “noise” in the proliferation of information (Tinkler et al., 1996, pp. 73- 74) (see also Bruce, & Candy, 2000). Programming logic and systems thinking will become increasingly important in the medical sciences as medical scientists interact with Experts Systems, programmed in scientific logic to troubleshoot errors, monitor quality, and interpret the clinical significance of laboratory test results (Gillies, 1996; Jackson, 1999; Sikaris, 2001). Elite or discipline and Discourse specific literacy is dependent on basic literacy in reading, writing, speaking and listening, and “new basics”, logic or critical thinking, including higher-order analysis, comprehension, and problem-posing and problem-solving (Lankshear, 2000, p. 112). From the socio-cultural perspective, elite literacy must also be understood in the context of a specific social practice, and what adds value to that practice, in innovation and improvements (p. 112). It is widely acknowledged that in the new work order geared for information and service this entails sophisticated “symbolic logical capacities” in knowledge work and symbolic analysis (Lankshear, 2000, p. 111) (see also Aronowitz, & DiFazio, 1994; Drucker, 1993; Gibbons et al., 1994; Reich, 1992). Once literacy is understood in these terms it is difficult to separate new basics or critical, and elite or Discourse literacies. For knowledge work and symbolic analysis in the medical sciences, the cultural and critical dimensions of literacy are fused, as are the new basics or critical, and elite or Discourse specific literacy types. But there are two aspects of critical thinking to consider. Experts are critical in discourses but are not necessarily critical of them (Chi, Glaser, & Farr, 1988; Gee, 1996; Schön, 1983), and it is necessary to clarify this distinction for knowledge work in socially accountable EBLM. Critical thinking in the medical sciences is discipline specific, based on the integration of many forms of scientific knowledge, and also transdisciplinary because scientific knowledge is inextricably linked with political, economic, social and other pragmatic factors. In laboratory test evaluations in EBLM, two forms of critical thinking are needed, in the medical science disciplines, and of medical science Discourse. The difference is explained by two perspectives in literacy theory, Olson (1994) who provides a scientific perspective, and Gee (1996) who provides a socio-cultural perspective. Olson (1994) explains that “literacy in Western cultures is not just learning the abcs, it is learning to use the resources of writing for a culturally defined set of tasks and procedures” (p. 43). This is revealed in studies that examine the role played 67 by literacy in “the evolution of particular cultural activities such as law, science, literature, religion, and philosophy” (Olson, 1994, p. 44). In the evolution of science, Olson explains, literacy has facilitated certain kinds of mental activity. Claims have been made that literacy engenders scepticism in readers because writing permits the accumulation of evidence and the search for proof (p. 52). Such scepticism is also present in oral cultures, but literacy has permitted the formalisation of evidence and proof by writing that exposes scientific ideas to discussion and critique. Literacy is both functional and social and requires competence with culturally specific scripts. Because “different scripts recruit different competencies”, to participate in a “textual community” is to share ways of reading, writing and interpreting texts (p. 273). To be literate in a domain is to share a “paradigm” (in Kuhn’s sense [1970]), shared texts, symbol systems, interpretations and beliefs as to what poses suitable problems and theoretical frameworks for research. Elite literacies are thus based in the ideology or values that define specific Discourses (Gee, 1996; Lankshear, 2000). According to Olson (1994), literacy can be understood in terms of critical thinking which turns thoughts into “worthy objects of contemplation” and ideas into “hypotheses, inferences, assumptions” which can be turned into knowledge by the “accumulation of evidence” (p. 277). Literate thinking is theoretical because it is necessary to consciously distinguish between the implications that follow logically from a theory, and the evidence that bears on the truth of those implications (p. 280). In the sciences, from reader/interpreter perspectives, evidence plays a crucial role in the assignment of “illocutionary force” or authorial intention to expressed propositions (p. 280). There are many possible interpretations of a phenomenon, and the “critical reader” derives implications for each of those ways and tests them against the available evidence (p. 281). Criticism of texts is an important part of thinking and explains the link between literacy and critical thinking (p. 275). There are different kinds of readers. Those who take texts literally based on presumptions about authorial intention will be operating within the operational and cultural dimensions of literacy, making rule-governed, Discourse-defined interpretations. Critical readers will be aware that there are alternative interpretations that may be taken (see also discussion of Eco’s model reader theory in Section 4.3.1). If literacy is defined too narrowly, Olson (1994) argues, competence is also narrowly defined. As a result, students who are successful in summarizing, remembering and reporting the gist of what they read, may be lacking in critical 68 interpretive abilities (Olson, 1994, p. 275). Science textbooks help perpetuate this situation if they deliver only fixed science content, and omit the epistemological debate and adjudication that helped to shape the content. This leaves students “to sort out the assumptions of the discipline, the definitions, the theoretical frameworks, the supporting evidence, the plausible conjectures and inferences” (p. 254) (see also Kuhn [1970]). Students who are given access only to facts and descriptions are limited to “surface readings” and literal meanings. This claim is supported by research into higher learning (Marton, Hounsell, & Entwistle, 1997; Ramsden, 1988, 1992), and justifies the inclusion of discussions of scientific logic and method in science courses (Matthews, 1998). A second angle on critical thinking in Discourses is given in socio-cultural perspectives, and based on the premise that there is little point in trying to understand literacy as an asocial cognitive skill (Gee, 1996). A “practice account of literacy” considers the social practices (Ideologies or Discourses) in which literacies are embedded (p. 57) (see also Lave, 1988; Scribner, & Cole, 1981). Literacy must be considered in many dimensions, operational, cultural and critical, depending on the concerns of particular social practices, medical science Discourse for example. The critical dimension of literacy can be clarified by making a further distinction between knowledge acquisition and knowledge learning (Gee, 1996). Knowledge can be acquired by apprenticeship and mastery in practice through a subconscious process, emulating an expert’s behaviour on the job (or it might be said by osmosis and trial and error) (p. 136). Learning about a Discourse requires a meta-cognitive process based on deliberate, conscious, and critical reflection (p. 138). Both mastery in practice and analytic reflective awareness of disciplines are needed (p. 139), which amounts to a distinction between learning inside a Discourse and learning about it (p. 136). The best way to learn about a discourse, Gee argues, is by juxtaposing one Discourse or discipline with another, especially if their ideologies or worldviews are conflicting. According to Gee (1996), people who are “bi-Discoursal”, have mastered two conflicting Discourses, are frequently the source of challenge and change (p. 136). People may be expert or critical in a Discourse, but are not necessarily critical of the Discourse. In laboratory medicine for example, experts will add value by improving methods, but additional abilities are required for socially accountable laboratory medicine. Crossdisciplinary courses and comparative reflection are thus needed in higher education to assist students to develop greater awareness of 69 different perspectives. In Gee’s perspective diversity of Discourse experience is a “cognitive necessity” and not an optional extra (1996, p. 141). This is because learning as acquisition in apprenticeship, and teaching as knowledge transmission, are not generally accompanied by active learning and critical reflection, and indoctrinate or “colonise” students who remain inside particular Discourses (p. 145). From the emancipatory literacy perspective, to learn is “to create and re-create” and not just repeat what others say, and this requires deliberate conscious reflection of what disciplines and Discourses have to say (Freire, & Macedo, 1987, p. 77). These views support the liberal-vocational ideal put forward in Section 3.3. In summary, the dimensions of literacy needed to add value to medical laboratory science, can be placed on a continuum, incorporating operational, cultural and critical literacy dimensions, entailing a range of competencies on a continuum between “d” and “D” competence. Medical scientists are critical in the medical science disciplines by ensuring that the correct culturally coded interpretations are made, and by criticism of medical science Discourse, ensure it gets challenged from the inside. The next section focuses on higher learning and expertise in order to find a way to explain how this range of literacies and competencies can be targeted more specifically in clinical chemistry.

3.5.2 A framework for knowledge work and symbolic analysis

There may be few opportunities for undergraduate medical scientists to become bi-Discoursal as Gee (1996) recommends, but meta-knowledge or critical reflection on the Discourse can be fostered from within, because the values of the Discourse are embedded in its abstract symbolic representations (Latour, 1987, 1990). Criticism of the Discourse begins with expertise or criticism in the Discourse in knowledge work and symbolic analysis. Laurillard’s distinction between “first order” experiential and “second order” academic knowledge explains the connection between expertise and symbolic analysis (1993, p. 58). First order knowledge is gained through first hand experience by direct action on objects in the world. People with operational competence have acquired first order knowledge of work, gained by apprenticeship to an expert on the job. Second order academic knowledge is acquired through deliberate reflection, integrating theory and practice, and mediated by representations or academic “descriptions of the world” in language, symbols, 70 diagrams and pictures, which link the theoretical and actual worlds (Laurillard, 1993, p. 58). Second order knowledge requires a “cognitive apprenticeship” so that the content of learning, or theory, is integrated with tools, objects and representations, as they are used in context on the job (Brown, et al., 1989). Meta-cognition is thus “situated cognition”, the term “cognition” being applied to emphasise that more than manual techniques are involved, and that authentic learning takes place if situated in the “nexus of activity, tool and culture” (p. 40). In this perspective, a “craft apprenticeship” is needed, along the lines of the “master craft” approach associated with the educational philosophy of John Dewey, entailing the integration of practical and theoretical knowledge (Chambliss, 1990, p. 68). To explain the mechanisms of higher learning, Laurillard (1993, 2002) opts for a structuralist perspective based on relations. The term “mathemagenic” (borrowed from Rothkopf, 1970) is applied to describe activities carried out by learners that are widely believed to induce learning (Laurillard, 1993, p. 48; 2002, p. 41). There are at least five learning activities likely to foster learning and help students relate knowledge to experience (Laurillard, 1993, p. 68). The first learning activity requires students to apprehend the structure of the discourse, and be able to organise the knowledge base into a coherent whole (signified-signified relations). The second learning activity requires the integration of the signified–signified relations with academic descriptions or representations (signs or more precisely, signifiers), as given to perception in words, symbols, diagrams, pictures, graphs and equations (signifier-signified relations). The third learning activity requires action on representations or academic descriptions of the world, relating structured knowledge to experience, or integrating theory and practice. The fourth activity requires adjustment of actions in response to feedback, and the fifth activity requires reflection on the whole “goal-action-feedback cycle” (Laurillard, 1993, p. 68). These activities are interdependent, being based as they are on the structuralist perspective. Taking a structuralist perspective, academic knowledge is relational in nature (see Gibson [1984], Piaget [1971], & Schwab [1962, 1964a, 1964b] for applications of structuralism in education). For a relational view of learning there must be a “what” and a “how” of knowledge that lies between knowledge content and learning. The “how” of learning is the “‘structural’ aspect”, the “what” corresponds to the “‘meaning’ aspect”, and in practice structure and meaning are fused (Marton, & Ramsden, 1988, p. 273; 1992, p. 44). In order to talk about the meaning attributed to 71 something it is also necessary to consider how meaning is constituted (Ramsden, 1992, p. 44). This distinction is explained by structural linguistics (Hjelmslev, 1943/1961; Saussure, 1959) in which a distinction is made between that which is signified (the “what” of learning) and the manner of the signifying (the “how” of learning). Meaning is constituted when concepts and their representations are interrelated in structures (explained further in Sections 4.2.1 & 4.2.2). The structure of scientific disciplines rests on moves in twentieth century science to grasp knowledge of things and theories in terms of their relations with other things and other theories in the same system or whole of which they are a part (Balzer, & Moulines, 1996; Schwab, 1962, 1964a, 1964b). Clinical chemistry content is both multi and transdisciplinary knowledge, because it comprises many concepts from physics, chemistry and biology that are indirectly linked with economic, political, and social forms of knowledge (e.g. Burtis, & Ashwood, 1999). Elements of clinical chemistry structure include theories of Electro-Magnetic Radiation (EMR) integrated with theories of light, magnetism and electricity; the classification of elements in chemistry according to atomic structures from which other elements and their properties emerge; and the physiology of life based on anatomical structures from which patterns are discerned permitting inferences to be drawn about life functions such as self-regulation (Piaget, 1971; Schwab, 1962). The significant properties of the parts of a system are acquired from their associations with other parts in the system. As the associations change so too their properties change. In physics for example, it is not so much facts about the properties of particles that are sought but the associations they make with other particles and their consequences. Understanding of the behaviour of systems permits prediction and brings about revision and change in a knowledge system (Schwab, 1962). Schwab identifies two broad patterns of thinking in the acquisition of these forms of scientific knowledge, the production of catalogues, and pattern recognition. Thinking in categories, or classificatory thinking, is based on observation and recording of aspects of matter or life structures organised into catalogues, taxonomies and descriptions. The discovery component, hypothesis generation or logic is demonstrated by creative observers who discern hidden patterns in observation data, leading to explanations and predictions, thereby advancing the knowledge system (the two approaches are demonstrated in Sections 7.2 & 7.3). 72

The curriculum implications of structuralism proposed by Schwab (1962) remain relevant in the twenty-first century (Laurillard, 2002; Ramsden, 1992). As Schwab (1962) recommends, a science curriculum should not be presented as a “highly esteemed body of knowledge consisting of collections of literal statements” corresponding to facts (p. 197). Scientific knowledge has rather, a conceptual structure, it forms a coherent whole comprised of interrelated components, theories, concepts, or signified aspects. Its organisation in structures facilitates observation, pattern recognition, proposition or hypothesis formulation, testing by experiment, and verification that leads to the formulation of scientific statements. It must also be emphasised that such structures are temporary, fragile and subject to revision, and that there is no single formula for the structure of a discipline (Schwab, 1962, p. 202). Each discipline has a particular conceptual structure, asks different questions, seeks different kinds of data and formulates bodies of knowledge differently (p. 203). The “syntactical structure” of a scientific discipline refers to its order of method, pattern of procedure, or how it goes about using its conceptions to attain its goals (p. 204). The traditional “schoolbook” version of the order of scientific method - observation, hypothesis generation and testing, and stating conclusions, does not represent the path of discovery and justification for every scientific discipline (p. 204) (see also Halliday, & Martin, 1993). In order to formulate the structure of a scientific discipline such as clinical chemistry, syntax, the order of method or pathways to verification, concepts and their interrelations are considered (as discussed in Section 6.2). The first thing to account for in knowledge work is thus the structure of knowledge, the conceptual structure of its component elements and their interrelations; and the syntactical structure, or order of method. The structure of content or signified aspects does not however give a full view of the structure of knowledge. In order to integrate the content of a knowledge system in practice, Laurillard (1993) argues, students need to apprehend its conceptual structure and also the way the ideas are represented in language, mathematics, diagrams and symbols, integrated in “sign-signified relations” (pp. 56-58) (see also Lemke, 2000). The distinction between representations of things, thoughts about things and things in themselves is a philosophical problem more than two thousand years old, present in the writings of the ancient Greeks, the Stoics, Plato and Aristotle (Eco, 1984). Subsequent philosophies also address subjective mind (Foucault, 1966/1970 traces this transition), and science philosopher Karl Popper has made a distinction 73 between three worlds, the world of objects, the world of the mind, and the world as created and represented with the aid of technologies and tools (in Popper & Eccles, 1981). “World 1” is the “real” world of things, “World 2” is the subjective world of the mind, and “World 3” is the world of abstract ideas and representation of things, tools and other products of the mind (p. 15). “World 3” is the new objective world created by humans, “the world of the products of the human mind, a world of myths, of fairy tales and scientific theories, of poetry and art and music” (p. 15). “World 3” consists of products of our own making, material constructs, tools and technologies, and ideas that materialise in artefacts such as books (p. 38). “World 3” is important for human progress because it increases our understanding. Physicists for example are primarily interested in “World 1”, but in order to learn about the world, they theorise using “World 3” objects as their tools (p. 47). As Olson (1994) explains for literacy theory, we live not so much in the world, as in the world represented to us in artifacts and “the topic of literacy is all about the peculiar properties of these artifacts” (p. xiii). Literacy is about the “kinds of competence, the forms of thought and the modes of perception” that are needed for coping with and exploiting the “world on paper” (p. xiv). Literate thought as it has evolved in the Western tradition is not so much about the world as its representations in “explicit statements, equations, maps and diagrams” (p. 277). There are studies that address scientific literacy specifically by looking at representations. Latour (1990) seeks explanations of learning that put “visualization and cognition into focus”, and take “writing and imaging craftsmanship into account” (p. 21). The world of science for Latour (1990) is revealed through two- dimensional paper “inscriptions” (and three-dimensional models) incorporating artifacts such as graphs, equations, tables, diagrams, photographs and pictures (p. 36). There is however much more to it than simply visualization, because scientists start “seeing” things when they “stop looking at nature” and start looking at inscriptions or representations (p. 39). Inscriptions are even more powerful than this because scientists use them to argue with one another (p. 21). A scientists’ “imaging craftsmanship” is likely to carry the day in the rhetorical situation when powerful financial allies are sought to provide funding for projects (p. 40). In this light, as Laurillard (2002) asserts, if students are to integrate theory and practice, they must learn to manipulate and interpret representations (inscriptions), and to reflect on the 74 experience in a critical manner. These commentaries identify what needs to be done in knowledge work and symbolic analysis, but not how it can be done. The “practice account of literacy” (Brown et al., 1989; Lave, 1988, Lave, & Wenger, 1991; Scribner, & Cole, 1981; Vygotsky, 1978), and cultural anthropology (Lévi-Strauss, 1966), reveal that there are no “global cognitive skills” such as taxonomic or classificatory thinking and syllogistic reasoning or logic, outside of specific contexts (Gee, 1996, pp. 54-57). Distinctions such as oral/literate and primitive/civilized are revealed in terms of first order knowledge or “science of the concrete”, that which is perceived and experienced, and second order knowledge or “science of the abstract”, that which is grasped through logical relations between abstract representations, symbols systems, charts, diagrams, maps, pictures, equations, and the object world. Scientific learning requires more than the acquisition of facts and experience, it requires deliberate reflection on academic descriptions or scientific representations in practice situations (Laurillard, 1993). The “precepts” or abstract symbolic descriptions of the world, its representation in models, schematic diagrams, graphs, charts and equations, must be integrated with the “percepts”, objects, present in the natural learning situation of the laboratory for example (pp. 28-29). If learning is mediated by integrating abstract knowledge in symbolic representations with the world of experience, by deliberate reflection on the experience, a theory is needed to explain how this integration can occur. The notion that ideas are not ideas unless represented has been explored in philosophy since at least the time of Plato and Aristotle (Eco, 1984); in the twentieth century by philosophers such as Nelson Goodman (1977) and Michael Polanyi (1969); and was given theoretical and methodological substance by nineteenth century semiotician and science philosopher Charles Sanders Peirce (1931-58). The logical triadic sign model, sign-object-interpretant relation developed by Pierce (as explained in Section 4.4.2), is akin to the concept of “tacit knowing” commonly attributed to Polanyi (1969). Analysts of work frequently apply the term “tacit knowing” to aspects of expert practice that cannot be readily explained, except in terms such as intuition (Aronowitz, & DiFazio, 1994; Barley, & Orr, 1997; Gibbons et al., 1994; Schön, 1983, 1987). Schön (1987) refers to intuition as that which is used by reflective practitioners in the “indeterminate zones of practice”, characterised by uncertainty, uniqueness and value conflicts that escape the canons of technically rational explanations (p. 6). Polanyi (1969) argues that the processes of 75 discovery and intuition demonstrated by scientists reflect their ability to distinguish signal and noise or figure from ground, directed by the clues they perceive in the environment (p. 109). The connection between objects such as symptoms and clues and the theory arrived at by a scientist is achieved by a “tacit integration” of visual perception and object recognition (pp. 181-182), referred to in semiotics as “sign production” (Eco, 1976). According to Polanyi (1969) there is a “structure of tacit knowing” that accounts for scientific discovery given by a triad that joins three coefficients, “akin to the triad of Peirce” (p. 181). For a thing B to have bearing on an object C, it must endow it with meaning. The triad thus consists in subsidiary things (B) (signs or representations) bearing on a focus (C) (object) by virtue of an integration (interpretation) performed by a person (A) (Polanyi, 1969, pp. 181-182). In this view trained perception is basic to all descriptive sciences, and the “logic of perceptual integration” is the logic of scientific discovery, which is not explicit but tacit (p. 139). There are ongoing disputes about the nature of scientific logic in the philosophy of science (e.g. Nickles, 1980; Popper, 1972/1979), but the tacit knowing hereafter semiotic perspective of scientific logic is useful for explaining many facets of knowledge work and symbolic analysis in the medical sciences. Analysts of work note that knowledge work is invisible craftwork because it resides inside peoples’ heads (Aronowitz, & DiFazio, 1994; McGee, 2002). Semiotics provides a theoretical framework for making knowledge work in the medical sciences more visible. It provides a way to structure professional knowledge in signs; and provides mechanisms through sign action, for the multi-literacies, competencies, logic and pragmatics applied by knowledge workers in laboratory practice. It can also be used to address contextual factors impacting on the knowledge work experience, and for social criticism of the rhetoric and ideology that underpins medical science Discourse. Semiotics is explained further in Chapter 4.

3.6 Conclusion

This chapter has explored the changes taking place in higher education and knowledge production, and some of the strategies used to advance higher learning in the medical sciences. It has been proposed that expanded views of competency, from “d” to “D”, operational, cultural and critical, are needed to account for the complexity of knowledge work and symbolic analysis conducted in informatised 76 laboratories, and in EBLM. It has been proposed that for scientific learning, there must be mediation between workplaces and universities, between the world of practical experience, and the world as represented in abstract symbolic descriptions in academia. In automated computerised laboratories, work can be characterised as symbolic analysis because much of it is focused on representations, graphs, charts and statistics. There is an additional function for symbolic analysis in social criticism as will be required in EBLM. Experts integrate abstract symbolic descriptions with theory in laboratory practice, and their knowledge work is not always visible. This chapter has converged on semiotic theory as a way to improve the visibility of the knowledge work of experts, in the structure of the disciplines, in the logic of practice, and in the multi-literacies used in manipulating representations. Semiotics can also define those aspects of knowledge work and symbolic analysis that can add value at work not just for employers but also for workers as lifelong learners. This topic is continued in the next chapter.

77

Chapter 4 A semiotic framework for Discourse and knowledge work

4.1 Introduction

For the purpose of making knowledge work visible, this chapter distils mechanisms from semiotics that can be applied to the structure of clinical chemistry knowledge, and its representation, the modes of reasoning used in laboratory practice, the constraints of the laboratory context on knowledge work experience, and social criticism in Evidence-Based Laboratory Medicine (EBLM). In the process it is demonstrated that semiotics has a wide application in many fields. Semiotics has a long history and its subject matter is very complex, and many branches have been derived in science philosophy and language philosophy, since its original conception in Greek medicine. The term “semiotics” is derived from the Greek “sêmiotikos” meaning significance (from the root sêma” meaning mark or sign) (Cobley, & Jansz, 1997, p. 4). In the philosophical tradition, signs are a way to theorise about thinking and behaviour, and to connect the external world of object reality with the internal world of the mind (Morris, 1971; Peirce, 1931-58). In the twentieth century, semiotics was developed as a tool of cultural analysis by the ordering of signs in terms of interrelations (Eco, 1976). Semiotics in this form is closely connected with the structuralist movement, based on the principle that things have meaning in terms of relations with other things coexisting in systems (Hawkes, 1977). Structuralism has uncertain origins, but came to prominence in physics at the turn of the twentieth century, and was adopted widely in linguistics and the humanities in the first half of the twentieth century. Structuralism in its various manifestations, seeks out the systematic basis of things through their whole-part interactions in systems, their representations, and their codification in culture (Gibson, 1984; Hawkes, 1977; Kurzweil, 1980; Piaget, 1971; Saussure, 1959). It is not within the scope of this chapter to deal with the philosophy of signs and representations. Signs are referred to adopting structuralist principles, in terms of their interrelations with other signs, with culturally coded meanings, and with the interpreters of sign systems, in “syntactics”, “semantics”, and “pragmatics” respectively (Morris, 1971, p. 21). It is important to note however that the semiotic 78 tradition is more than two thousand years old and various branches have been developed by different knowledge systems for different purposes, many of which have great bearing on the medical sciences. These include symptomatology in medical semiotics; symbolic logic in analytical philosophy and information theory; structural linguistics, socio-linguistics and social semiotics which give insights into sociology and other social science and humanities disciplines; behavioural semiotics and logic in the natural sciences; and bio-semiotics in the life sciences (Cobley, & Jansz, 1997; Eco, 1976; Sebeok, 1994) (Figure 4.1).

c. 400BC Hippocrates & Greek medicine Medical Semiotics c.200AD Symptomatology Galen c. 300BC Plato & Aristotle The Stoics c. 300AD Augustine Logic, language theory & sign theory c. 500AD Boethius c.1000AD Abelard Roger Bacon c.1300AD Ockham

Rationalism Empiricism c.16-17C Descartes c.16-17C F. Bacon, Hobbes c.17C Leibniz, Spinoza c.17C Locke Symbolic Logic c.18C. Vico c.18C Berkeley, Hume c.18C. Natural history, Linnaeus c.18-19C. Philosophy, Kant & Hegel c.19C Boole, Frege, Peirce

Structural linguistics Pragmatics & Behavioural c.19-20C F. de Saussure semiotics Analytical philosophy c.19C C.S. Peirce c.20thC Russell, Carnap, c.20C Ogden & Richards Whitehead 20C Hjelmslev, Jakobsen c.20C Charles Morris IT & AI Eco Biosemiotics Structuralism von Uexküll Lévi-Strauss, Barthes Sebeok Lotman Poststructuralism Sociolinguistics & Thom Derrida, Foucault, Social semiotics Barthes Halliday, Bernstein

Figure 4.1. A history of structuralism and semiotics.

The aim of this chapter is to distil a framework that encompasses the structure of a knowledge base, the logic of practice and the rhetoric and values that underpin communications in knowledge systems. The framework will demonstrate that structure, logic, and rhetoric (syntactics, semantics and pragmatics respectively), can 79 be synthesised in one semiotic model (Eco, 1976). This synthesis is achieved by integrating structural linguistics (Saussure, 1959) and scientific logic (Peirce, 1931- 58). This chapter is organised into two broad sections, structure and logic, based on linguistic and scientific semiotic systems whose fundamental sign architectures are similar but different. Structural linguistics is based on a dyadic sign model (percept- concept, signifier-signified relation) (Saussure, 1959), and scientific logic is based on a triadic sign model (percept-concept-object relation) (Peirce, 1931-58). Section 4.2 deals with structures, beginning with structural linguistics (Hjelmlev, 1943/1961; Saussure, 1959), and its application to the structure of non-linguistic objects (Barthes, 1964/1973), and the consumer experience of objects and places (Baudrillard, 1968/1996; Gottdiener, 1995). Section 4.3 deals with logic and pragmatics, as developed from the sign triad of Peirce (1931-58), to explain how structures are put to use in practice. Different aspects of the framework will be tested in its application to clinical chemistry knowledge base, laboratory context, laboratory practice and communications in subsequent chapters.

4.2 Structuralism and semiology

Before explaining the connection between structural linguistics and semiotics, it is noted that the term “semiology” is sometimes applied to the structural analysis of non-linguistics objects (Barthes 1964/1973, Saussure, 1959), but the term semiotics was adopted for usage in all avenues of semiotics in 1969 (Eco, 1976, p. 30). The term semiology is retained in this chapter in order to draw a clear distinction between structures and the way structures are used applying logic and pragmatics. This section focuses on structural linguistics, not to provide a review of studies in linguistics per se but to clarify the structuralist method, as the basis of scientific method, and to demonstrate its general applicability in many fields. The origin of structuralism as a movement is very complicated and uncertain due to the many meanings given to the term, and its wide variety of use. Structuralism provides a methodological principle that has been widely applied in the natural sciences, mathematics, social sciences, humanities, linguistics, and literary criticism (Balzer, & Moulines, 1996; Foucault, 1981/1991; Gibson, 1984; Kurzweil, 1980; Lechte, 1994; Medawar, & Shelley, 1980; O’Farrell, 1989; Piaget, 1971; Sturrock, 1979). Structuralism was also a broad intellectual movement representing an alternative to 80 phenomenology, Marxism and existentialism in the 1950s and 1960s. In this form, structuralism avoids dealing with subjectivity and individual self-consciousness, because the unconscious, stable structures of the mind, and the systematic, codified basis of cultures are considered to be more important (Kurzweil, 1980, p. 3). For example, kinship systems, totems and myths explain the way individuals are constrained to the codes of culture (Lévi-Strauss, 1966). This form of structuralism was subjected to heated debate and criticism and was all but defunct by the late 1960s, but its central tenets remain firmly entrenched (Kurzweil, 1980, p. 10). There are certain core principles recognisable in all forms of structuralism ranging from the methods adopted in the physical sciences to reconstruct theory (Balzer, & Moulines, 1996), to the methods adopted by sociologists that reveal class structures and social controls hidden in classrooms in the pedagogic code (Bernstein, 2000). Gibson (1984) provides a comprehensive summary of the nature of systems or structures in six related principles (pp. 8-12) (see also Piaget, 1971, p. 5). Firstly, structures are whole systems comprised of interrelated parts, so that structures determine behaviour and not individual parts. Secondly, the relations between the elements in the system are more important than the elements themselves. Thirdly, the individual or human subject is displaced from the centre of things, so that “mankind” not “man” becomes the object of study in the human sciences, and all individuals are at the mercy of codes. Fourthly, structures are preserved or “self-regulated” to ensure the system’s survival even though change does occur. Fifthly, the structuralist method is synchronic and emphasises structure in preference to evolution. This point is misleading however, because all forms of structuralism are concerned with systems in transformation. The final point therefore is that structures are evolving, systems are structured and also structuring, subject to adaptation due to changing conditions, but at the same time their fundamental and systematic structure is retained. These six principles are represented to varying degrees in the wide range of structuralisms applied in the natural and social sciences. In structural anthropology for example, the stable systems of culture are exemplified by kinship systems, incest taboos and food restrictions, and are captured in totems and myths (Lévi-Strauss, 1966). In linguistics there is a stable system of grammar operating at the unconscious level that determines language use (Saussure, 1959). In the physical sciences, structuralist principles are deliberately targeted in attempts to reconstruct theories from those already existing (Balzer, & Moulines, 81

1996). The form of structuralism that most people will know, although it is not usually labelled as such, is that which is widely applied in the biological sciences, and captured in the terms “division”, “classification” and “system” (Black, 1952; Cohen, & Nagel, 1934; Foucault, 1966/1970; Harré, 1960; Piaget, 1971; Popper, & Eccles, 1981). A system is described by analysis or division into elementary parts, and the parts are then ordered into categories based on similarities and differential features, in a synthetic or classificatory procedure. Linnaeus, for example, analysed plant forms by division, and then classified them based on distinctive visible features, into families, genera, and species, in order to set up the botanical taxonomy of natural history (Foucault, 1966/1970, p. 134). Classification requires comparative analysis which is a fundamental aspect of all structural analysis, but the move from catalogues to systems, from structures to functions, requires a much more difficult, sometimes intuitive, sometimes logical manoeuvre (p. 139). Piaget (1971) provides a few examples of the shift in thinking from catalogues in natural history to life systems in biology. In the nineteenth century, Claude Bernard proposed the mechanism of physiological “homeostasis” to explain self-regulation and feedback in living organisms. In the twentieth century, Dobzhansky explained genetic homeostasis, Waddington explained the theory of genetic assimilation of organisms with environment, and Jacob and Monod explained the “latency” and “intermittence” of gene function in the “operon” theory (see Piaget, 1971, pp. 47-51). As Schwab (1962, 1964a, 1964b) asserts, a grasp of structure is crucial for understanding in the natural sciences (see also Laurillard, 2002; Ramsden, 1988). Gibson (1984) makes a similar point for education in general, because structures underpin the systematic basis of things including mind, body, society, and culture as given in language, literature, and myths, and also the natural world and mathematics (p. 8). Structuralism in the natural sciences is concerned with the physical world and theory, so that the sphere of culture is not considered. Structural linguistics is given particular attention in this section because it clarifies the structuralist method; incorporates signs and representations by focusing on the forms in which languages are expressed; and links the internal language system or grammar, with factors outside natural language, institutions, nations, geography, history, psychology, and sociology (Hjelmslev, 1943/1961). This makes it particularly useful for analysing the structure of transdisciplinary (Mode 2) knowledge systems such as EBLM. Structuralism in the semiological form is explored in four stages in this section. The 82 term semiology is retained despite the wide acceptance of the term semiotics, to distinguish structures from their use, by mechanisms of logic, as explained in Section 4.3. There are four stages of development in semiology to consider. The first stage deals with the internal language system (Saussure, 1959); the second stage clarifies and generalises the semiological method, and adds external features to the internal language system (Hjelmlsev, 1943/1961); the third stage demonstrates the application of semiology to non-linguistic objects (Barthes, 1964/1973; Baudrillard, 1968/1996); and the fourth stage explains how these methods are applied in sociological analyses of consumer experience of objects, tools and environments, particularly educational environments (Gottdiener, 1995). Scientific analogies with linguist categories are given where possible, and cross references are made to its application to clinical chemistry Discourse, knowledge base, and laboratory context in Chapter 6.

4.2.1 The internal structure of language

Saussure (1959) was the first to consider language as a self-contained system, the interdependent parts of which function and acquire value through their relationships in the whole language system. This structured, synchronic approach to language provided an alternative to traditional approaches to language which focused on the historical, evolutionary aspects of language (Baskin, 1959, p. xii). Saussure (1959) devised his language theory in the late nineteenth century and presented it in a series of lectures in Geneva early in the twentieth century. Students in Saussure’s classes pulled his lectures together soon after his death (see preface by Bally, & Sechehaye in Saussure, 1959). Although Saussure’s work was well known to the Russians early in the twentieth century (see Voloshinov, 1929/1994), it did not reach a wide English speaking public until Baskin’s English translation was published, and so all references to Saussure carry the date, 1959. There is no intention to give Saussure’s language theory detailed treatment in this section. The discussion is limited rather, to a few key principles associated with Saussure’s synchronic theory of language because structural linguistics, beginning with Saussure, has clarified the method of structuralism applied widely in many fields (Eco, 1976; Gibson, 1984; Hawkes, 1977; Kurzweil, 1980; Piaget, 1971; Sturrock, 1979). Four major issues raised by Saussure are given consideration. Firstly, language is a structured system 83 existing independently of individual speech acts, at the unconscious level. Secondly, the significant units of language are signs and so language is a system of signs. Thirdly, each part of the language system draws its meaning according to relations of similarity and difference to other parts in the system. Finally, the system of language resides in the tension between arbitrary chance like associations in language which carry the risk of chaos and failed communications, and the conventions of language acquired through use that lead to its social organisation and motivation. These four points are dealt with in this section in order to demonstrate the structuralist principle that everything derives meaning from relations, whole/part and part/part structured relations in sign systems. The first idea is that language is a structured system existing despite individual speech acts and history. To explain this idea, Saussure (1959) structured his linguistic ideas around the “bifurcation” that distinguishes between linguistic structure and linguistic usage (p. 9). Distinctions are thus drawn between the internal structure of language which is relatively stable (Langue), human speech which is evolving due to external factors (Langage), and individual speech acts (Parole); that is between the state of a language, its grammar, which is static and unchanging (Synchrony), and the evolutionary changes that arise due to speech (Diachrony) (p. 79). Saussure draws an analogy with plant biology to illustrate the point. The transverse sections of plant fibres reveal relations between the fibres that are hidden when only longitudinal sections are considered (p. 88). Although it is difficult to separate the synchronic and diachronic aspects of language, Saussure emphasised the synchronic aspects of language because that aspect was missing in historical language studies, which, in fragmenting languages, failed to apprehend the structure or whole in which the parts coexist (p. 98). Language belongs to the individual and to society, it is “a social product of the faculty of speech”, a collection of conventions the social body adopts which enables the individual to exercise the faculty of speech. Langue, the state of language, is a self-contained system whose interdependent parts function and acquire value by their relationships in the whole system (Saussure, 1959, p. 9). Langue is a homogeneous system with structure and existence independent of any history of language, social institution (Langage), or sign consciousness given in the everyday speech acts of individuals (Parole) (p. 13). Speech (Langage) is heterogeneous because it straddles several domains, encompassing physical, physiological, 84 psychological and social aspects. Internal linguistics abstracts language as a grammatical system with internal arrangements governed by rules. Saussure notes that the language system is analogous to the game of chess (as does Wittgenstein, 1953). The fact that chess passed from Persia to Europe is external and has no effect on the game that depends on internal rules (Saussure, 1959, p. 22). Likewise in language, everything outside the language system, including history, psychology, institutional factors, and individuals, is excluded from the object of synchronic language study (p. 20). In a quintessentially structuralist move, Saussure aimed to uncover “a profound unity in language”, a system, despite the evolution of language and the diversity of idioms (p. 99). This unity has its counterpart in biology in concepts such as physiological and genetic homeostasis that explain the way life functions (see Piaget, 1971); and in cultural anthropology, in concepts such as kinships systems, totems and myths that explain mechanisms of culture (Lévi- Strauss, 1966). Such unity can only be discovered if certain minimal changes are ignored, in the same way that mathematicians disregard infinitesimal quantities in logarithmic calculations. The concept of a language state is thus an approximation, which as in most sciences requires simplification of data (Saussure, 1959, p. 102). The second point in Saussure’s language theory to consider, derives from his opposition to long held notions that language can be reduced to a simple naming process, that a name corresponds directly to the thing named, because he argues, this implies that ideas exist before words (Saussure, 1959, p. 65). In the language system, the linguistic unit is a double entity, it is not a name united with a corresponding thing (Figure 4.2a), but a concept and a sound image, neither physical nor material, but a psychological impression of sound (p. 66) (Figure 4.2b). These two elements, concept and percept, are intimately united in the linguistic sign, as illustrated for the word arbor meaning “tree” (Figure 4.2c).

Concept Signified (Sd) Arbor = “Tree” Sound image Arbor Signifier (Sr)

Name Sign (a) (b) (c) (d)

Figure 4.2. The linguistic sign as dyad (adapted from Saussure, 1959, p. 67). 85

A sign is not merely a representation or sense impression, in the case of language, a word sign is a sound image coupled with an idea. Thus, the Latin word arbor carries the concept “tree”, its sensory aspect or the sound image arbor calls forth the appropriate association “tree” (Figure 4.2c). This link is arbitrary, any other word could have been assigned, and the connection between the sign and the actual tree becomes a cultural convention (p. 67). Once signs are reduced into their concept, sound image components, they become abstractions, and the concept becomes a psychological entity for investigation by psychology, and the sound image becomes phonic substance for investigation by physics and physiology (p. 103). Saussure eliminates the ambiguity between representation and sign, the two terms are often confused, by assigning the three terms, “signifier” (Sr) for the expression or sound image, “signified” (Sd) for the concept, and both come together in the linguistic sign (Sd/Sr) (p. 67) (Figure 4.2d). For Saussure (1959), language is “organised thought coupled with sound”, otherwise thought would be chaotic (p. 111) (the same applies to gestures and sign languages but Saussure was mainly interested in sounds [see Eco, 1976, p. 10]). Without language, thought is a “vague uncharted nebula”, a shapeless indistinct mass, and the “linguistic fact” can be pictured as “a series of contiguous subdivisions (signs) marked off on the indefinite plane of jumbled ideas (thought) and the equally vague plane of sounds (Saussure, 1959, p. 112) (Figure 4.3).

Sign A B C D

Thought

Sd a b c a1 1 1 d Sr b c Language d1

Sound

Figure 4.3. Sign model of language. 86

Language represents the “domain of articulations” between these two planes, its role is to link thought and sound (p. 112). Linguistic signs enable us to clearly distinguish between ideas, there are no pre-existing ideas, and nothing is distinct before language appears (p. 111). Language works by a double move, and this is the basis of the structural semiotic method. On the one hand, sound and concept are united in the sign (Sd/Sr relation) for example a/a1 (Figure 4.3). On the other hand the signs function in relation to each other, so that language is a system of interdependent terms in which the value of each term results from the simultaneous presence of the other terms (Saussure, 1959, p. 114). The subdivisions marked off in the domain of language indicate a string of signs: a/a1, b/b1, c/c1, d/d1 co-existing in linear relations of contiguity. This string could be a series of words in a phrase or clause, although not in a sentence, which is characterised by freedom of combination, thereby belonging to linguistic usage (p. 124). This double move in language distinguishes signification or meaning (Sd/Sr) and linguistic values (relations between signs) (Saussure, 1959, p. 114). As Guiraud (1975) explains, there are “two sides to the coin of knowledge” (p. 54). One side is signified, epistemological and associated with meaning or signification based on semantic Sd/Sr relations. The other side is signifying and associated with linguistic values based on relations between signs (see also Ramsden’s distinction between the “what” and the “how” of knowledge discussed in Section 3.5.2). Saussure’s synchronic language state is particularly concerned with this latter aspect of language, in particular Sr/Sr relations, the articulated relations between the forms of language (phonemes or sounds), and not language as actual sound or phonic substance, which is subject matter for physics (Saussure, 1959, p. 122). It is not enough to consider signification, the concept-sound image, Sd/Sr relation, because a concept or a sound means nothing in itself, except in its relations to other concepts (Sd/Sd) or other sounds (Sr/Sr) in terms of conceptual and/or phonic differences (p. 120). Saussure explains this idea using an analogous situation involving the exchange of money and goods. On the one hand dissimilar things can be exchanged, one thing for another for which a value must be determined. For example bread can be exchanged for money, say a $2 coin (analogous in language to significance or meaning), but the value of the exchange can only be determined when the $2 coin is compared with similar coins ($1, 50 cents, 20 cents, or notes). In other words, other 87 forms of money co-existing in the same monetary system confer value on the particular form of money being considered (p. 115). In a similar manner, words and ideas are exchanged. Word signs however are complicated, they change values when their component elements are exchanged (sounds, letters and syllables), but the same signification can emerge from two different word forms. This is exemplified in different languages, for example, tree, arbor, and arbre, all signify the same thing in different forms, thus illustrating the arbitrary principle in language. In summary, in language, there are signified-signifier (signs) and signifier- signifier (forms) relations to consider, and the relations between signs are not the same as the relations between their component elements, signifier-signifier or signified-signified. The observable relations between different signs A, B, C, D, are distinct from the relations between their component sounds (a1, b1, c1, d1) or component concepts (a, b, c, d) (Figure 4.3) (Saussure, 1959, p. 115). Saussure uses the terms “opposition” and “difference” to illustrate that the mechanism of language is based on relations between contrasting signs (Sd/Sr) and contrasting forms (Sr/Sr). That is language is based on “oppositions” between signs (Sd/Sr), and on phonic (Sr/Sr) and conceptual (Sd/Sd) differences within them. Whereas the value of a sound (Sr) is determined by what it is not, that is difference, between signs (Sd/Sr) there is only opposition or distinction (p. 121). Meaning arises from conceptual oppositions with other signs, so that whatever distinguishes one sign from another also constitutes it as a sign (p. 121). Gordon (1996) illustrates this difference using the example of the waterways system. The words, “ocean”, “lake”, “river”, and “creek”, are distinct signs referring to bodies of water (p. 48). As signs, they each have their significations or meanings (Sd/Sr relations), but acquire their “values” by comparison with the other signs in the waterways system, in terms of conceptual oppositions (similar but different). Thus, the sign “lake” acquires its linguistic value as an inland body of water once it is opposed to, or compared with the sign “ocean” which is a vast sea. On the other hand, the words, “lake”, “bake”, “cake”, and “make” belong to different sign systems, and have contrasting forms due to a change in one of the elements of each word, which produces an entirely different meaning in each case (p. 49). The same principle that distinguishes value and meaning also distinguishes forms from each other and creates meaning (p. 48). Similarity and difference as described by Saussure, gives insights into the variant/invariant principle on which classifications in the natural sciences are based (Cohen, & Nagel, 1934; 88

Medawar, & Shelley, 1980; Popper, & Eccles, 1981). The variant/invariant distinction is elaborated in Section 4.2.2 in Hjelmslev’s (1943/1961) linguistics, and Section 4.2.3 in Barthes’ (1964/1973) semiology of objects. The third point in Saussure’s language theory to consider is that the language system is based on relations. The relations between signs in conceptual oppositions and between sign elements (Sd & Sr) in terms of difference, enable the language system to be understood as a form of “algebra”, and so signs have function in relation to each other (Saussure, 1959, p. 124). The relations and differences between linguistic terms fall into two distinct groups each generating a class of values, and each class of relations and differences corresponds to two forms of mental activity, reasoning in linear series, and reasoning by association (p. 123). Division of an object for analysis reveals on the one hand, linear relations in language because its fundamental units (letters and words) are chained together in syntagms (from the Greek syntagma meaning chain) (p. 123). The syntagmatic relations in language are “in praesentia”, observed by their relations of combination when two or more terms combine in linear series (p. 123). On the other hand, there are non-linear associative relations formed outside the present discourse “in absentia”, and representing a “potential mnemonic series”, the seat of which is in the brain as “part of the inner storehouse that makes up the language of each speaker” (p. 123). That is, each term or element in a linear series evokes associations with the possible terms that are absent, by associative relations (note that Hjelmslev refers to associative relations as “paradigmatic” [1943/1961, p. 39]). Associative relations and the classification of languages are derived from significant points in the linear series or points of invariance, as will be explained in Section 4.2.2. The fourth point in Saussure’s language theory to consider is that there is a systematic and contradictory principle that allows the language system to function, based in the arbitrary link between signified and signifier in the linguistic sign. The language system is on the one hand fixed in social conventions and rules of grammar, which protect the language system from modification except by deliberate acts of reflection (Saussure, 1959, pp. 71-78). On the other hand, the mechanism of language is not simple, it is “a partial correction of a system that is by nature chaotic” (p. 133). There are degrees of arbitrariness between relative and absolute extremes, between chance like associations which would result in chaos and failure in communication if applied without restriction, and the principle of order and 89 regularity or structure that is at work in the human mind at the unconscious level (pp. 131-133). There are “solidarities” in language that bind the syntagmatic and associative relations in the language system and limit arbitrariness, as is indicated in certain restrictions on syllable formations and vowel and consonant relations (Hjelmslev, 1970, p. 35). The arbitrary/motivated distinction, the basis of Saussure’s language system, is complex and resides in the structuralist proposition that the mind is structured at the unconscious level (Saussure, 1959, pp. 131-133). Saussure worked out the synchronic aspects of language by working with linguistic forms, sound units or phonemes, entirely from within the internal system of language. The next section explores the way Hjelmslev (1943/1961) expands on internal language theory, firstly by addressing the mutual correlation between expression forms (signifiers) and content forms (signifieds); and secondly by bringing external factors, history, geography, and ideology within the sphere of language through the mechanism of connotation.

4.2.2 Internal and external linguistics

Hjelmslev’s linguistic model is addressed in this section because, although he was concerned with the structural analysis of the forms of language, he made it clear that his prelude to a theory of language was applicable to objects other than verbal language, and could be projected onto the physical, physiological, psychological, logical, and ontological “reality” outside language (Hjelmslev, 1943/1961, p. 8). Three aspects of Hjelmslev’s language theory are considered, external linguistics, linguistic algebra for dividing and classifying objects, and the mechanism of connotation that facilitates the move between internal and external linguistics. Each aspect is important because it is used for the analysis of clinical chemistry Mode 2 transdisciplinary knowledge, laboratory spaces and instruments, and the connotations of laboratory instruments that raise social implications for laboratory work experience (In Sections 6.2 & 6.3). Hjelmslev’s method applied to objects is clarified by Barthes (1964/1973) in Section 4.2.3, and links are drawn between connotative semiotics and logic by Eco (1976) in Section 4.3. The first aspect of Hjelmslev’s language theory to consider is the expansion of Saussure’s language sign model (signified-signifier) to encompass all aspects of culture. Saussure’s structural linguistic program works with the internal language 90 system, based on sign Sd/Sr and Sr/Sr relations, so that language has form but not substance (Saussure, 1959, p. 113). Hjelmslev (1943/1961) begins his language analysis with a phenomenon, “purport” or matter, existing provisionally as an unanalysed amorphous mass waiting to be ordered by different languages, discourses or disciplines (pp. 50-52). In the expanded cultural sign model (given graphic representation by Eco, 1984, p. 45), Hjelmlsev substitutes the language specific terms “signified” and “signifier” with the more general terms “content” (C) and “expression” (E), each having “substance” (S) and “form” (F) (pp. 50-52) (Figure 4.4).

Phenomenon Substantive content

Substance Form SIGN = Signified Content = Conceptual structure Signifier Expression Form Substance Object morphology Purport/matter/continuum

Figure 4.4. Cultural sign model.

In natural languages for example, the sound image has form (form of expression) according to the grammatical relations it enters into with other language forms. It also has phonic substance (substance of the expression) that can be investigated by other sciences, such as phonology, physics, and physiology. Sound thus becomes a general phenomenon or purport waiting to be ordered by the various sciences or languages, and the analysis in each case provides a basis for the comparison of disciplines. The matter or purport is inaccessible to knowledge, it can only be known once it is given form on analysis by particular languages or disciplines. There is thus no universal formation of a phenomenon, only a “universal principle of formation” (Hjelmslev, 1943/1961, p. 76), the structuralist principle. It is the function of each language or discipline (substance of the content) to determine how this occurs (form of the content). Conceptual fields or disciplines form the substance of the content differently depending on what they are trying to achieve. Hjelmslev’s cultural sign model is readily adaptable to Schwab‘s criteria for the structure of disciplines (1962, 1964a, 1964b). For example, the unstructured 91 phenomenon, light, is given substantive structure by physics as Electro Magnetic Radiation (EMR) (substance of the content), which is ordered conceptually to different theories in physics (forms of the content) and given technological application and mathematical expression (substance and form of expression) (as demonstrated in Section 7.2). Comparisons of disciplinary perspectives are facilitated by the structuralist method, and enhance understanding of a phenomenon. For example, light, will be ordered differently by the art community which may be more concerned with its formation as a perceptual field, sensually or experientially, and sometimes these fields, art and physics, draw inspiration from one another (as discussed by Medawar, & Shelley, 1980). Whereas understanding of a phenomenon such as light is the business of non- linguistic sciences such as physics, it is the task of internal linguistics to describe the form of the expression or content, and to project that form onto a substance, or the substantive content of a conceptual field. Internal linguistics conducts analysis on an internal and functional basis examining the component elements or expression forms of an object for analysis as they coexist in mutual correlation with content forms (Hjelmslev, 1943/1961, pp. 78-79). This point presumably provides the logic for the placement of the forms inside the graphic cultural sign model (Figure 4.4), and also clarifies a point of confusion present in the English translation of Hjelmslev (Eco, 1984, p. 45). As Eco explains, “expression purport” and “content purport” to which Hjelmslev refers, are derived from the same continuum, and it is possible to argue this point in the light of the “dynamic object” in the Peircean triadic sign model, as an unordered phenomenon waiting to be ordered from particular disciplinary perspectives (Eco, 1984, p. 44) (discussed in Section 4.3.2). The second aspect of Hjelmslev’s language theory to consider is the interrelation between expression forms and content forms in “linguistic algebra”, which focuses on “sign-functions”, the mutual correlations between two or more expression-content relations (Hjelmslev, 1943/1961, p. 96). The “algebra of language”, also referred to as “glossematics”, is based on internal relations and designations of expression and content elements that are motivated once a language system is confronted with a substance for analysis (p. 80). Analysis is conducted of expression forms in linear series (syntagmatic relations in the expression line), and the potential associations arising at significant points in the linear chain (paradigmatic associations in the expression side). The presence of a significant unit 92 in the linear series, one that changes the meaning of a chain, is established by “commutation”, the mutual correlation between elements in each plane (Hjelmslev, 1943/1961, p. 59). Linguist algebra can be used to explain the principles of scientific division, classification and system. It allows Hjelmslev to claim that all sciences can be centred on linguistics, because linguistic algebra provides the logic for exhaustive description of any substance whatever (p. 78). In language analysis, analogies are drawn with the physical and biological sciences and mathematics, and terms such as “variant”, “invariant”, “commutation”, “permutation”, “mutation”, “catalysis”, “restraint”, “constraint” and “calculus” are frequently used. Whereas the structuralist method ultimately aims at defining a general system, in some circumstances it is enough to apply the methods of division and classification in order to improve comprehension of an object in question. Two aspects of language analysis, derived from Hjelmslev (1943/1969, 1970) are now demonstrated to illustrate the difference between the analysis of word forms, the forms of expression from which genetic relationships between languages are derived; and their mutual correlations with culturally coded contents or meanings that demonstrate how the structure behind language is derived. Both approaches to analysis can be applied to objects as will be demonstrated in Section 4.2.3. The first example of language analysis is classificatory and demonstrates how genetic relations between languages are derived from their classification based on similarities of language usage, the Indo-European language family for example, which includes Italic, Celtic, Germanic, and Hellenic languages (Hjelmslev, 1970, p. 68). Genetic relationships are demonstrated when there are certain expression elements, words, syllables, letters or sounds in common, and a rule emerges which permits languages to be grouped (p. 13). A genetic relationship is demonstrated between the Romance languages, French, Italian, Spanish, and English for example, in their respective usage of the words mère, mater, madre and mother. The rule on which the classification is based is that the letter m appears at the beginning of each word (p. 14). Genetic relationships based on expression elements cannot however account for all words in a language (p. 89). A linguistic typology is needed to explore mechanisms of language usage in widely differing cultures, to show what linguistic structures are possible, why those structures, and not others, ultimately to explain how linguistic change comes about (pp. 91-96). Such a typology is based on categories such as vowels and consonants and the relations of combination and 93 association they enter into in word signs (p. 95). In structural linguistics it is proposed that these laws reside within the disposition of the language system itself (p. 131). That system is based on the mutual correlation between expressions and contents. The second example of language analysis aims to derive understanding of linguistic usage using linguistic algebra, the mutual correlation between expressions and contents, which provides the “general calculus” Hjelmslev (1943/1961) proposes is useful for detailed analysis of any object whatever (p. 9). In language theory, Hjelmslev aims to devise a systematic procedure by which objects can be described “self-consistently and exhaustively” (p. 15). This is based on the premise that every “process” has an underlying system, and can be analysed into a limited number of elements, arranged in a linear series in relations of combination (syntagmatic or expression line) (division); and in a vertical system according to relations of possible associations (paradigmatic or expression side) (classification) (p. 9). The terms “syntagmatic” and “paradigmatic” are applied specifically to verbal language, and the terms “process” and “system” are applied in general, to the linear and associative planes of analysis (p. 39). A language text represents “datum” that can be subjected to a “deductive progression” and analysed into linear chains (syntagms) and non- linear associations (paradigms) once significant points of variation in the chain are identified, that is points of invariance. The text is thus regarded as a class that can be analysed into components that are in turn classes that can be analysed into further components, and the process continues until the analysis is exhausted (p. 13). The procedure is thus analytic and specifying, empirical and deductive, rather than synthetic, generalising or inductive (p. 13). Analysis can be restricted to exhaustive description in order to facilitate knowledge or understanding of the object in question (p. 15). The exhaustive reduction of objects in this manner allows them to be organised in systems around a leading principle inductively, not by trial and error or accidentally (p. 20). Division, classification and system are fundamental strategies in the natural, particularly biological sciences (Black, 1952, Cohen, & Nagel, 1934; Foucault, 1966/1970; Schwab, 1962). However, the statements derived in the process tend to receive more attention than the strategies used to derive the statements. The analysis of a language text for example, begins with an expression form (Text or message) (Figure 4.5), which is reduced to its constituent significant features, word signs and syllables, and distinctive features, syllables and letters. In 94 order to extract the systematic basis of language, the expression units derived from analysis are considered for their mutual correlation with culturally coded content units.

External linguistics & Internal linguistics/Linguistic schema Linguistic usage

The individual, history, culture, society eg Sign pat (F of E) Text Message Plane of expression Paragraph or Sign strings Sentence Expression line - expression elements, figurae, letters or phonemes Clause p •a •t p • e • t Sign = Word or syllable s • a • t r • a • t e.g. Pat-ern-al t • a • t S Expression side

C F Alternatives E F S

Figure 4.5. Linguistic structure.

Hjelmslev (1970) notes it is difficult to work with the language sign system by reducing it to word signs. This is because words are frequently complex, comprised of several signs that signify independently of the given word. The word pat-er-nal for example contains three syllables including the sign pat, which may be a name or a verb, as well as a syllable not functioning as a sign (p. 32). By introducing the category of “figurae”, language is analysed as a system of figurae, distinctive units or elements used to construct signs (Hjelmslev, 1943/1961, p. 41). Figurae or distinctive elements, occupy specific places in word chains, clauses and sentences that make up messages or texts. It is peculiar to verbal languages that the number of distinctive units or figurae is limited, on average twenty (e.g. the 26 letters of the Greek alphabet) (Hjelmslev, 1970, p. 37). In order to gain insights into the systematic basis of language, the sign pat is used as an example (Figure 4.5). Pat, derived from further reduction of the sign paternal, derived from a message, is further reduced to the figurae, p, a, and t, as the distinctive elements participating by relations of combination in a linear chain or sequence (syntagmatic axis or 95 expression line). Under each of these figurae or elements, a vertical axis of potential elements exists by association (paradigmatic axis or expression side). Under the element p for example, there is the possibility of substitution of elements s, r, and t, to form the new signs sat, rat and tat respectively (p. 33). The vertical axis p, s, r, t thus represents the axis of associations drawn from the elements stored in memory, Saussures’ storehouse of possible concepts (Saussure, 1959, p. 123). What is discovered in language analysis in this manner is that there are restrictions on the possibilities of sign formation, and certain elements will be inserted only in certain places. The language system prevents a sign from having any appearance whatever (Hjelmslev, 1970, p. 34). In English, potential signs such as pgt and pkt are not formed in accordance with English rules of syllable formation (p. 34), and other syllables that are sign possibilities such as pid and maf are not, by chance, actually used (p. 36). Note that these observations will be linked with physiological characteristics of speech organs (Saussure, 1959). No further analysis of language is considered in this section, except to emphasize the features important in its application to objects (in Sections 4.2.3 & 7.2.2). The ordering of objects in the plane of expression is determined by commutation of significant elements (invariants) existing in mutual correlation with elements in the plane of content. As Hjelmslev (1943/1961) explains, the plane of expression alone provides not much more than nomenclature (p. 58). The sign is an entity generated by the expression-content relation, it is appropriate to speak not of signs but of “sign functions” (p. 48). Expression and content presuppose one another and exhaustive description requires consideration of the way two or more expression- content sign functions enter into mutual correlation. There are points in the expression line occupied by “invariants” that call up alternatives from the paradigmatic axis by association. This process is referred to as commutation, the means by which new signs are generated (p. 73) (e.g. pat, sat, and rat in Figure 4.5). The variant/invariant principle applies to figurae, letters or distinctive units in two ways. If the vowels a, and e are invariant, two different signs are created, pat and pet respectively. However, the element a may be intended but pronounced differently, sounding like e, or i, in which case a sound substitution has occurred, a variation of form without a change in content (Figure 4.5). Confusion in communication arises with such substitution, as occurs when a second language is spoken with an accent. The variant/invariant principle also applies to words, for example, the word signs 96

“wood” and “tree” are variants in Danish and can be substituted for each other because they have the same content or meaning, but “wood” and “tree” are invariants or significantly different in French and German because wood refers to forest of which tree is but an element (Hjelmslev, 1943/1961, p. 74). The analyst thus seeks from the linear series points of difference or invariance (p. 59). It is the identification of variants (different forms same theme) and invariants (different forms, different themes) in language theory that makes exhaustive analysis possible (p. 60). In summary, the expression line and the expression side together make up the plane of expression, which can in effect be infinitely expanded. A text belongs to external linguistics or linguistic usage, but for the purposes of analysis, language appears first as a system of signs, and by its reduction to sentences, clauses, word signs or syllables, an internal structure or linguistic schema is revealed that can be further reduced according to linguistic algebra (Figure 4.5). In order for analysis to be systematic, there must be two or more signs interacting, and analysis can be conducted initially from either perspective, the plane of expression (expression line plus expression side), or the plane of content. In linguistics, analysis begins with the plane of expression, and the relations between forms (phonemes, letters) demonstrate “mutual solidarity” with language contents (p. 59). In the physical sciences, structural analysis is concerned with the analysis of the plane of content, the interrelations between theories, with the purpose of reconstructing theory (Balzer, & Moulines, 1996). The expression forms are in these cases technological applications and mathematical equations. The primary function of linguistic algebra is analysis and description, not synthesis and generalisation (Hjelmslev, 1961, p.12). However, the principle of commutation and linguistic algebra can provide a basis for identifying that which is general and systematic in language. Language analysis rises above mere description if the analyst discerns a pattern or general system that can be used to predict events or explain phenomena. The term meta-semiotics is applied to descriptions of the way scientific statements are generated, but is not generally within the purview of linguistics (p. 114). Neither linguistic algebra (analysis in the plane of expression) nor meta-semiotics, both being scientific, can provide insights into the external linguistic realm of individual psychology, society, history, politics, or ideology. This requires a connotative semiotics (p. 114). 97

The third aspect of Hjelmslev’s language theory to consider is connotation. Second order connotative language builds on primary first order denotative language which correlates planes of expressions with planes of content, by designating factors outside the system of language (Hjelmslev, 1970, p. 136). Connotation is demonstrated when a semiotic expression-content relation becomes an expression of a second semiotics, referred to as “connotative semiotics” (Figure 4.6, adapted from Eco, 1976, p. 55) (see also Barthes, 1964/1973, p. 90).

ad infinitum

E C

E C Secondary connotation

ECPrimary denotation E = expression C = content

Figure 4.6. Connotative semiotics (adapted from Eco, 1976, p. 55).

A connotative semiotics is non-scientific because there are an infinite variety of pragmatic circumstances that influence the way expressions are interpreted (Hjelmslev, 1943/1961, p. 119). Alternatively, a “meta-semiotics”, produced when the primary expression-content relation becomes the content of a second semiotics, is scientific because it is concerned with contents as logical statements and propositions that may be subjected to verification within the scientific language in question (p. 119). As Hjelmslev (1970) explains, there are many degrees of language or “meta- languages” (p. 132). First order languages are denotations that describe an object language, second order languages are meta-languages that describe first order languages, and third order languages are connotations that describe second order languages and so on. It is by these “degrees of grammar” that geographical, historical, social, and psychological aspects of language are brought into the sphere of linguistics (p. 136). Using Hjelmslev’s example, the Danish language relies on denoted expressions correlated with contents, and these expression-content relations in turn become expressions of the Danish nation, family, home and individual character. Ultimately a language speaks the historical, psychological, cultural and political discourse of a nation (p. 136). In rhetorical discourse, the style of language 98 becomes the expression plane of another language, and gives insights into the ideology behind the language (p. 136). As Eco (1976) explains, stylistic devices or figures of speech are rhetorical expressions used to manipulate language (p. 279), often in ways that mask alternative contents, thereby producing the biased statements of ideological discourse (p. 290). Because connotative semiotics extends language analysis to the rhetorical and ideological functions of language, Hjelmslev (1943/1961) claims a “key-position” for linguistic theory in knowledge because it encompasses the individual and the whole of human society behind language (p. 127). Saussure (1959) and Hjelmslev (1943/1961) emphasised that all objects could be subjected to textual analysis in a manner similar to language analysis, but they both stayed with linguistics and gave no other applications. Saussure (1959) introduced the term “semiology” to make the distinction between linguistic and non- linguistic object analysis (p. 16). There are two approaches to the semiotics of non- linguistic objects that demonstrate its application to ideological Discourse. Barthes (1964/1973) provides general guidelines for semiological analysis and demonstrates its applicability to the “system of fashion” (1967/1990). Eco (1976) gives structure to connotative analysis, and logic is introduced by applying the triadic sign model of Peirce (1931-58). Reasoning is from sign to sign with a pragmatic purpose in mind, so that the infinite regress of connotation, or semiotic drift is avoided, constrained for the purposes of Discourses (Eco, 1976, p. 69; Eco, 1990, p. 30). Eco’s approach is explored in Section 4.3, and Barthes’ semiology of objects is given detailed treatment in the next section.

4.2.3 Semiology and systems of objects

Roland Barthes (1964/1973), a literary theorist and general cultural analyst, set down the elements of the method of semiology applicable to non-linguistic objects, and applied it to the system of fashion (1967/1990). This, he acknowledged, was an “operation” fuelled by his “personal, ascetic” goal to be scientific in reconstituting the “grammar” of an as yet unanalysed language, which would also be useful for teaching semiology (Barthes, 1967/1990, p. ix; 1984/1988, p. 5). This section gives an overview of Barthes’ approach to object analysis incorporating sociological and anthropological perspectives. The method provides several levels of 99 analysis, analogous to Hjelmslev’s degrees of language, including objective descriptions of objects, primary denotations, and secondary meanings or connotations, implying the social significance of objects to users. A similar approach is demonstrated by cultural analyst Jean Baudrillard (1968/1996) who applies semiological analysis to everyday household objects. Baudrillard avoids the objective structural and functional description of objects, and focuses on technological progress and the consumer experience of objects at psychological, ideological and social levels. A brief exploration of Baudrillard’s analysis is conducted in Section 4.2.4.2, because it provides insights into how to approach the analysis of spaces such as laboratories, over and above their technical and functional characteristics. Socio-semiotic analyses of environments and everyday objects draw inspiration from Barthes and Baudrillard among others, in order to understand how spatial arrangements of environments can impact on consumer experience (Gottdiener, 1995). In such analysis, objects with use values and exchange values, acquire symbolic values, in a complex set of interactions between consumers, producers and the objects of consumption. Objects such as household furnishings and clothing are imbued with sign values that surreptitiously manipulate consumers and their choices as they bid to gain social status, but consumers manipulate producers as well (Gottdiener, 1995, p. 41). It is noted however that these kinds of analyses require knowledge of sociology, and are not easy to reproduce, because they are dependent on the creativity of an individual cultural analyst operating on the plane of connotations (applications are demonstrated in Section 4.2.4). Socio-semiotic approaches to material culture analysis nonetheless demonstrate ways to gain insights into the socio-ideological aspects of consumer experience that are applicable to laboratories (as demonstrated in Section 6.3). The semiology of objects begins by outlining the levels of analysis required, objective, denotative, and subjective connotative.

4.2.3.1 Semiological levels of analysis

Barthes (1964/1973) declared that every use or function of an object is given meaning in terms of the verbal explanations given (p. 10), so that semiology is a part of linguistics, a “trans-linguistics”, a “copy of linguistic knowledge”, applicable to non-linguistic objects (p. 11). Barthes’ semiology is based on the premise that 100 anything of social significance in the world, pictures, objects and patterns of behaviour, signifies through the “relay of language” (Barthes, 1984/1988, p.180). This is a reversal of Saussure’s proposal that language analysis would be part of a general science called semiology (Saussure, 1959, p. 16). Barthes’ placement of language above all else is contested (see Gottdiener, 1995; Kress, & van Leeuwen, 1996), but his method remains useful. The differences between linguistic and non- linguistic systems of analysis are explained with each step of his method in this and the next two sections. The purpose of object analysis is to gain knowledge of a society through analysis of its symbolism and the sign values, functional, visual or verbal, implied in its objects. Wherever there is a social order, functional objects are pervaded with secondary meanings, so that cultural, symbolic, or sign values are implied as well as explicit utilitarian values (Barthes, 1964/1973, p. 41, 1984/1988, p. 180). Semiology can be used to analyse cultural phenomena such as food, fashion, cars and home furnishings in an exhaustive manner. This is accomplished by building a “simulacrum” of the object of interest, “an intelligible assemblage of objects”, applying the universal structuralist principles, division, classification and system, in order to discover an organised whole from an amorphous collection of details (Barthes, 1964/1973, p. 95; 1967/1990, p. 16). The facts about objects and other cultural phenomena are subjected to analysis and distributed along the axes of meaning, expression line or syntagmatic axis, and expression side or paradigmatic axis, so that the object in question becomes ordered in the plane of expression (as explained in Section 4.2.2). Objects are thus structured objectively at the technological level denoting their primary functions. Objects however carry more meaning than function, and the second aspect of semiological analysis is essential, analysis of the connotations implied in objects, for example, wealth, status, and amusement, for what they reveal about a community and the experience of object users (Barthes, 1984/1988, p. 182). A distinction is made between the functional significance of objects, and what objects communicate indirectly to users through the social system in which they are used. The connotations of objects are discerned in rhetoric or stylistics, the way things are expressed. One level of analysis in semiology, the level of the proposition or statement, the “terminological system”, is avoided because it belongs to the language in question (Barthes, 1967/1990, p. 33). This is the scientific level of the proposition or statement that Hjelmslev refers to as 101

“meta-semiotic” (1943/1961, p. 120). There are three levels of cultural analysis to address in the analysis of objects including scientific laboratory objects, denotative objective descriptions, scientific propositions or statements, and connotations, and the analysis is capped by a fourth level, the analysts’ operation (Figure 4.7). Whereas the analyst “caps” a denoted message with connotation, this does not exhaust its possibilities because it is just a fragment of ideology that continues ad infinitum (Barthes, 1964/1973, pp. 90-91).

IV E C Analyst’s operation

III E C Rhetorical system

II Terminological system E C Real system I E C

Figure 4.7. Levels of analysis (adapted from Barthes, 1967/1990, p. 293).

Because the objects of semiology are mixed forms (signifiers or expressions) having material substance (substance of the expression), as opposed to verbal language which is analysed as form without substance (form of the expression), Barthes (1964/1973) argues that a “ternary system” of simultaneous, staggered levels is needed to determine how mixed signifiers communicate (p. 89) (see also Barthes, 1967/1990, p. 27) (Figure 4.7). The first level constitutes the “real” system, the object of analysis, which is objectively described for its morphological characteristics, structure and function. The second level constitutes the “terminological” system of denoted propositions about the real system given by natural languages and the meta-languages of the sciences (Hjelmslev’s meta- semiotics). The third level is implied or connoted, “set in motion” by the denoted objective and terminological systems. It constitutes the rhetorical system because values always underpin the levels of spoken language and propositions. It is social, affective, and correlated with worldly circumstances, its form is rhetoric, and its content is ideology (Barthes, 1967/1990, p. 33). The third level of analysis is unstructured, and is used to extract secondary meanings from objects in order to draw out implications of social significance. Because connotation continues ad 102 infinitum, analysis is conducted according to the criterion of pertinence identified by each individual cultural analyst (Barthes, 1967/1990, p. 16). This point connects with logic and pragmatics in the Peircean perspective (as discussed in Section 4.3). A simple scenario provided by Barthes (1967/1990), the teaching of the basic highway-code, demonstrates the levels that apply in cultural analysis (pp. 29-31). At the primary denotative level of signal, red ≡ stop, yellow ≡ caution, and green ≡ go (where “≡” = coded equivalence) (Figure 4.7, Level I), but the semantic relations between these perceptible colours, their relative meanings, if they are to be deciphered, must be taught at the level of language and the proposition, that is red signifies danger and stop (Figure 4.7, Level II). However speech is never neutral and the delivery of a message in teaching is rhetorical and ideological, there are intonations and inflections that can give insights into the teacher’s values and moods (p. 31). For example, the teacher may deliver the message in monotone, enthusiastically, or sarcastically and inferences may be drawn about the teachers’ attitude towards students and teaching (Figure 4.7, Level III), although that which is inferred is ultimately a matter of the analysts’ opinion (Figure 4.7, Level IV). The analysis of fashion (Barthes, 1967/1990) is conducted at two analytical levels, the real object system as structured by the “vestimentary code” or the “order of garments”, and the rhetorical system connoted by “Fashion”. The terminological system is omitted because, Barthes claims, it belongs to linguistics (p. 51). At the first level, the real garment system is subjected to division into the parts of clothing ensembles, which are in turn ordered into members of alternative possibilities in clothing by paradigmatic association. The analysis is thus conducted at the level of forms and substances in the plane of expression, and clothing items are inventoried, ordered into catalogues, and classified into “genera and species” (p. 94). In the process a coherent system of dress emerges, although the main purpose of applying this method to fashion is to promote reflexive awareness about cultural values and the ideological significance implied in the structure of clothing. This second aspect of analysis is more important, however the connotative or rhetorical system is not easily accessed, it requires a creative “operation” by the analyst, a “fugitive analysis”, directed towards the analysts’ particular concerns (p. 292) (Figure 4.7, level IV). Objective analysis is explained further in the next Section, 4.2.3.2, and subjective or connotative analysis in Section 4.2.3.3. On application to clinical chemistry and the laboratory, semiological analysis will yield objective descriptions 103 of the laboratory and its instruments, propositional statements about the use of laboratory objects, and ideological connotations implied in laboratory spatial arrangements and objects, for what they communicate to laboratory scientists (Sections 6.3 & 7.2.2). Ultimately the laboratory can be understood as a technological fragment occupying a place in a wider discourse of Health. In the case of scientific disciplines, the second level of analysis avoided by Barthes, propositional statements, must be considered because the validity of applied science activities is dependent on the validity of the scientific statements on which they are based (as discussed in Section 6.2).

4.2.3.2 Objective analysis: The denoted system

There are two main aspects to consider in the ordering of objects in the plane of expression, demonstration that a significant unit is present by the commutation test and the identification of invariants; and identification of the signifying matrix, the point in an object from which the signification emerges. Firstly, commutation is considered. Barthes (1964/1973) applies the commutation test to the analysis of non-linguistic objects to determine if a significant unit is present. This is done by artificially introducing a change of sign or element in the plane of expression and observing whether this change brings about a correlative modification in the plane of content (p. 65) (refer to Section 4.2.2). As Barthes explains, such analysis allows the analyst to “spot by degrees, the significant units which together weave the syntagm”, thereby preparing the way for the classification of those units into paradigms, and their ordering into families, genera and species (p. 66). The analysis begins with the object in question (e.g. clothing ensemble; or language text) and is conducted according to a syntagmatic analysis of the chain of elements juxtaposed in relations of combination (expression line) (e.g. trousers • shirt • cardigan • accessories, where “•” = relation of combination) (Figure 4.8). The expression line is inspected at each point in the chain in order to detect the significant points, or invariants, the points at which the introduction of an alternative element existing potentially by virtue of association, by commutation, changes the signification or meaning (e.g. shorts versus trousers; T-shirt versus shirt; and pullover versus cardigan). The expression side emerges from the significant units isolated in the expression line because the potential for signification resides with 104 alternatives, conceptual opposites, at the points of invariance (p 79). The alternatives exist in relations of association (similar but different) with each significant point, thus constituting the expression side of paradigmatic associations (a ≠ a1 ≠ a2 etc.) (Barthes, 1964/1973, p. 59).

Text/object a • b • c • Expression line component parts ≠≠ ≠ a1 b1 c1 ≠≠ ≠ Expression side alternatives

a2 b2 c3

Figure 4.8. The plane of expression (adapted from Barthes, 1964/1973, p. 67).

The rules of combination or “implication” between elements or units vary between different “languages”, fixed in some cases and relatively free in others (pp. 69-70). In the system of dress for example, the structured whole of a garment ensemble, “trousers, shirt, cardigan, accessories”, can be analysed in the plane of expression, and although there is a degree of freedom in combining the items of clothing, a code of dress dictates the appropriate combinations of clothing. A laboratory scientist will have very little freedom to recombine the components of a laboratory instrument, unless an inventor, but a certain degree of freedom to rearrange the instruments in the laboratory if in a managerial position. A change in a term in an expression line not effecting a change in signification is a variation of form not an invariant. A variant form is correlated with the same content, and has the same meaning and is therefore merely a substitution, as indicated in the language example between different forms of pronunciation (as demonstrated in Section 4.2.2). Hjelmslev (1943/1961) applies the terms variant and invariant to distinguish between a substitution (different form, same meaning) and a mutation (different form, different meaning); Barthes (1967/1990) applies the terms variation and variant. In scientific texts the term “invariant” is applied to identify a constant or a point of significance (see Cohen, & Nagel, 1934; Medawar, & Shelley, 1980; Popper, & Eccles, 1981). The identification of spatial units existing in relations of combination and virtual opposition at the points of invariance provides the basis for an inventory of component elements from which, by commutation, a system of categories and their classification into genera and species becomes established (Barthes, 1967/1990, p. 105

94). There are no claims made in semiology that the commutation process can be subjected to rational proof, it is rather, an empirical procedure, a mechanism of analysis that accounts for certain regularities, similarities and differences (p. 63). Any object subjected to this treatment is “unveiled according to a certain order” and according to the goals of the analysis (p. 16). Also the use of the term opposition is not intended to refer to binary oppositions such as black/white, it is used rather as Saussure intended (1959, p. 121), to oppose one cultural object to another for similarities and differences in terms of distinctions (Barthes, 1964/1973, p. 79). The object is thus described using the structuralist principle of division and classification according to differential features at points of significance or invariance, and ordered into categories under the headings of genus and species (Barthes, 1967/1990, p. 87). Similar strategies will be demonstrated in Linnaeus’ inventories and catalogues of natural history (as explained by Foucault, 1966/1970). Note however that commutation describes situations in biochemistry that have been subjected to so- called rational proofs. For example, when a variation in nucleotide bases in the genetic code produces a substitution of amino acids in proteins, either to no significant effect (and thus a variant), or resulting in a significant mutation and the possibility of serious disease such as sickle cell anaemia (and thus an invariant) (Mathews, & van Holde, 2000). Significant units will be identified in a similar manner in the analysis of laboratory instruments, as the points from which comparisons in the classification of instruments are made. These are also the points at which the logic of instrument use can be observed, because they are the points from which signification emerges (Section 7.2.2). The second point to consider in the analysis of objects, the signifying matrix, demonstrates the way object analysis differs from language analysis. Verbal language can be reduced to distinctive units, letters and phonemes so that the analysis of a text can be conducted exhaustively (Hjelmslev, 1943/1961). The objects of semiology or non-verbal language analysis are composed of mixed signs, including matter, words, and images, so that exhaustive analysis is impossible, it is necessary to be selective by working with fragments. As Barthes explains (1967/1990), only a few “utterances” of the signifier/object in the plane of expression need be chosen to establish the presence of invariants or mutations (as opposed to variations or substitutions) which bring about a change in the value or meaning 106 accorded to an object in question (p. 60). Object analysis begins with a fragment from which is built up a picture to characterise certain aspects of culture. Whereas verbal language is analysed at the level of forms (sound units or phonemes, letters, words and syllables) (form of the expression), the objects of semiological analysis are non-linguistic objects that have material substance (substance of the expression), and so analysis accounts for both the form (morphology) and substance (material aspects) of the expression (Barthes, 1964/1973, p. 34). Because objects have a material element, there must be a support for the signification arising at points of invariance, providing the basis for the object’s comparison with similar but different objects belonging to the same conceptual system. The analytical category of the “signifying matrix” is used to account for the complexity and material substance of the expression of non-linguistic objects (p. 60). The concept of the signifying matrix is important because it connects (perhaps unwittingly) with the logical semiotic model of Peirce (as discussed in Section 4.3). In verbal language, the system breaks through the linear chain or syntagm at every point, so that the syntagm is a continuous chain of significant units. By contrast, non-linguistic objects are discontinuous, the significant points are scattered sporadically throughout the chain leaving inert spaces (p. 68). An analogy can be drawn here with the genome of life forms that has stretches of non-significant material (at least as it is commonly believed) between the significant points marking the genes (Mathews, & van Holde, 2000). In the signifying matrix, the object (O) finds a support (S) for the signification, which is an aspect of the object that is invariant, the point of invariance (V). All technical objects (O) in their signifying function include supports (S) and invariants (V) (applied to laboratory instruments in Section 7.2.2). The invariant, drawn from a fund of potential oppositions (alternatives), is the point of significance, the point in the matrix from which signification emerges (Barthes, 1967/1990, p. 66). Invariants are referred to as “phonemes” (sound units) in spoken language; “graphemes” (letters) in written language (following Saussure); “gustemes” in the food system (following Lévi-Strauss); “technemes” in the case of technological objects (following Baudrillard); and “vestemes” in the fashion system constitute the invariants of clothing (Barthes, 1967/1990, p. 66). Whereas the support (S) testifies to an object’s technical existence (O), the invariant testifies to its signifying existence (V) (p. 66). In the structure of clothing, consider the ensemble, “trousers, shirt, 107 cardigan, and accessories”. The garment “cardigan” enters into relations of combination and association in this clothing ensemble, and several signifying matrices are possible, for example, cardigan (O) • collar (S) • open (V) ≡ sporty, as opposed to cardigan (O) • collar (S) • closed (V) ≡ dressy (p. 61). The collar is the point of invariance, the support for the signification, and the invariants, open and closed, signify the conceptual opposites, sporty and dressy. Open and closed are non- material invariants and the collar is the material support that stands between the object and the signifying element. Thus one element receives the signification - the object cardigan (O), another - the collar (S) supports it, in that it is the aspect that is varied, and the way in which it is varied (V) constitutes the signification (V) (p. 62). The analysis of invariants is a form of comparative analysis based on a system of similarities and difference leading to a system of categories from which genera and species are derived (Barthes, 1967/1990, p. 98). This process is applicable to any type of object as demonstrated in the biological sciences (Black, 1952; Cohen, & Nagel, 1934; Foucault, 1966/1970; Harré, 1960). It does not however, substitute for the quantitative study or analysis of variance (ANOVA) of conditions occurring between multiple groups as conducted by statistical analysis to levels of significance or probability (Burns, 1997). The aim of semiology is to distinguish units not count them (Barthes, 1967/1990, p. 11), as Linnaeus distinguished living forms by similarities and differences in classifying the objects of natural history (Foucault, 1966/1970, p. 134). Laboratory instruments are structured in this manner with the goal of isolating the significant conceptual points at which the logic of laboratory instrument use can be demonstrated (Sections 7.2.2 & 7.3). It must be emphasised however, that the structure of garments in the plane of expression, is not the primary goal of semiological analysis, which is rather, to draw out the economic, psychological and sociological implications of systems of signification such as food, fashion, and furniture through connotative analysis (Barthes, 1964/1973, p. 96; 1967/1990, p. 9). Ultimately cultural analysis aims at identifying social values and ideologies implied or connoted in the rhetorical strategies or stylistics in the morphology of material forms such as clothing. These too can be discerned in laboratories and instruments in order to give insights into the socio-ideological aspects of laboratory experience and medical science as technical Discourse (as elaborated in Section 6.3).

108

4.2.3.3 The connotations of objects

The rhetorical or connoted system is revealed in unstructured analyses used to gain insights into socio-ideological aspects of culture in fashion, food, furniture, theme parks, shopping centres and architecture and so on (Figure 4.7, level III). At the denotative level, the analysis of objects signifies function, the signs of use values are structured in the assemblage of objects analysed in the plane of expression. The system of dress for example, is organised along the two axes of meaning, syntagmatic arrangements of clothes in ensembles, and paradigmatic associations of possible alternatives. At the simplest level, the choices of various items of clothing within a particular ensemble may signify formal or casual attire, while choices among whole ensembles might signify class, cultural, or ethnic differences. The system of dress thus becomes a system of signification and the dress codes uncovered at this level are quite revealing about individuals and their social status and also differences between cultural groups. More meaning however, can be uncovered from semiological analysis by superimposing the system of fashion, as communicated through television talk shows, magazines and advertising, upon the system of dress. The fashion system is an intentional system of communication that aims to manipulate consumer choices using what Barthes refers to as “logo-techniques” (1964/1973, p. 32), thus illustrating another point of divergence of object semiology from linguistics. In natural languages there are arbitrary linkages between sign structures and their use (schema and usage, langue and parole, language as system, and language as spoken). The rules of grammar in the internal system of verbal language are only relatively arbitrary because they are caught up within a complex set of interactions between speakers as individuals, and speakers as part of a social system, affected by external conditions such as geography, climate and institutional factors. Speech is not possible without the language system and vice versa (Barthes, 1964/1973, p. 31). In semiological systems such as clothing, fashion, cars and furniture (and laboratory instruments), deciding groups are the originators of cultural systems. They impose “signifying constraints” on consumers of cultural commodities according to criteria such as power, vested interests, and pertinence, and are thus ideologically driven. This point is as applicable to laboratories as to mass cultural systems such as fashion. Semiological systems are artificial, “fabricated languages”, 109 using ideological devices, “logo-techniques”, to manipulate consumer choices, in the case of clothing, through the signs of fashion (Barthes, 1964/1973, p. 32). Although power structures and market forces frequently override individual choices, logo- techniques are in turn subject to the wider collective field of experience, community acceptance and economic factors that influence their acceptance or disappearance from the sphere of culture (p. 32). The semiologist aims to penetrate non-verbal languages using the same reductive methods used by linguistics, but with the particular purpose of uncovering secondary languages operating at the ideological level. The significance of these secondary languages will not be discovered by analysis into distinctive units (letters, phonemes) and significant units (words, monemes, signs), but from the reconstitution of reduced units into “larger fragments of discourse” (p. 11). This is the function of Hjelmslev’s connotative semiotics (1943/1961, p. 114). Gottdiener (1995), taking a sociological perspective, explains that logo- techniques exemplify a “knowledge-power-culture triple articulation” and are ideological mechanisms of power, normalisation and control (p. 39). The sign function of use, explicit at the denotative level becomes a hidden sign function at the connotative level. In Western culture, Barthes (1984/1988) explains, these hidden signs become naturalised so that phenomena of culture are converted into “pseudo- nature” and signs of ideology in a society (p. 190). It is paradoxical then that a society actively constitutes systems of signification co-existing in networks of semantic relations, and then displays equal activity in the other direction by attempting to mask the sign system through naturalisation and rationalisation (Barthes, 1967/1990, p. 285). Signs are thus everywhere in use but not seen unless actively sought, as they are by semioticians (Eco, 1976). Barthes (1967/1990) wrote his analysis of fashion in the 1960s at a time when fashion statements were appearing, for example, “this year dresses will be worn above the knee”, that provoked a mass response in the form of the mini-skirt, displayed on the streets, in magazines, clothing boutiques and department stores. Consumers are expected these days to be more sophisticated and less easily led, and the situation more complex, due to mass education and the exposure of logo- techniques through various avenues to the study of culture (Bennett, 1998; Gottdiener, 1995). Nonetheless dress codes still produce a variety of appearance choices that are constrained by the “tyranny of fashion” operating through the 110 language of fashion reporting and visual media. Logo-techniques are still used to manipulate people to adopt certain styles of clothing in order to look fashionable, and for social status and acceptance (Gottdiener, 1995, p. 21). The “signifiers of appearance” such as seductive advertising images are still linked with “signifiers of status and desirable psychological states” (p. 21). The next sections provide illustrations of various approaches to the analysis of material culture, food, household objects and educational contexts, with the common theme that they are for the most part ordered around Hjelmslev’s cultural sign model, and influenced by Barthes’ semiology. The purpose of this exercise is to gain insights into ways to approach cultural analysis of the laboratory and the contextual constraints impacting on laboratory practitioners’ experience.

4.2.4 Material semiotics and applications

Gottdiener (1995) synthesises the ideas of Hjelmslev, Barthes, Eco, Peirce, and Foucault, in his proposed socio-semiotic model of culture incorporating material semiotics, semiotics of objects, spatial semiotics and sociology (pp. 27, 138). Socio- semiotics is applied by Gottdiener to the analysis of consumer experience of material culture in Disneyland, shopping malls, the architecture of urban design, and real estate signs, structured around Hjelmslev’s cultural sign (Figure 4.4). Gottdiener (1995) considers pragmatic aspects of culture (following Peirce) by describing the function of spaces and objective experiences of consumers (pp. 67, 71), in addition to uncovering the hidden meanings communicated or connoted in material culture, as it articulates with the belief systems or ideology of a culture (pp. 29, 56). Particular attention is given to the articulation between ideology (form of the content) and material forms (substance of the expression), and the distinction between codified signification and intentional communication (p. 59). Thus, first order denotations or significations based on a stimulus-response effect from indexical signs are given equal weight with second order connotations or communications of intentional meanings from symbols. Emphasis is given to denotations, the pragmatic aspects of culture, often missing in idealistic sociological analyses of culture that gloss over the functional significance of objects, their use values and pragmatic purposes (p. 65) (e.g. Baudrillard, 1968/1996). Thus the use value of a leather coat, its primary purpose to keep out the cold, will be acknowledged in socio-semiotic analysis, as 111 well as its symbolic content to communicate social status as a sign of wealth, or pretension to wealth by the wearer. Socio-semiotics also draws inspiration from Foucault (1969/1972) whose archaeological method provides a means to explore peoples’ experience of particular spaces and the way their bodies are regulated and constrained. The experience of institutional practices and regulations as manifested in spaces is illustrated in Foucault’s analysis of mental institutions (1965), medical clinics (1963/1973) and prisons (1979) (Gottdiener, 1995, p. 71). This approach to culture enables the analyst to explore the way people are subordinated to discourses in spaces that are constraining and enabling in various ways (Foucault, 1966/1970, p. xiv). Socio- semiotic models attempt to explore all aspects of culture, and give equal emphasis to sign use and sign production, as to sign systems. They also consider the feedback mechanisms imposed on producers and objects by consumers, the three-way producer/object/user relations, and the forms of resistance by consumers, to the ideological manipulations perpetrated by advertising through various communications media (Gottdiener, 1995, pp. 177-180). The articulation between material forms (substance of the expression) and regulatory discourse (form of the content) can be extrapolated to all cultural environments. Analysed in this way, a pathology laboratory will be described as an index of its pragmatic function for the stimulus-response mechanisms and behaviour of users at the technological level (Gottdiener, 1995, p. 67); and as symbolic of the ideological forces that constrain participants operating within medical laboratory science, understood as a technical Discourse (see Section 6.3.4). The laboratory can be considered as both a system of signification and communication and its material forms and substances can be explored for the way they articulate with “codified ideologies” (forms of the content), codes of practice, social norms, power structures and institutional rules which are implied in the material structures of environments, buildings and objects (p. 28). As Gottdiener explains, this is based on the premise that material forms are never just matter with morphological characteristics, “they are encoded by ideological meanings” engineered into forms. At the same time “codified ideologies” are more than “discursive relations”, they materialise in the social order as “interactions, modes of appearance, designs of environments and commodified cultural objects” (p. 28). A technical Discourse such as medical laboratory science can thus be considered in terms of ideology materialised in 112 instrumental forms and laboratory spatial arrangements. A socio-semiotic analysis will be structured and unstructured, and give insights into the way laboratory spaces and objects impact on individual and group experiences, constraining them to a technically rational experience. Laboratory objects have use values, exchange values, symbolic and sign values, and participate as components in medical science Discourse (substance of the content), formed specifically according to its codification in practice (as ideology or form of the content), and materialising in the object world of the laboratory (substance of the expression) (Figures 4.4 & 6.1). The next three sections illustrate the wide applicability of semiological and socio-semiotic analysis, by its provisional application to the food system, and the way it has been applied to household objects and educational institutions and environments.

4.2.4.1 A semiological approach to the food system

The phenomenon of food has functional, technical, social and symbolic aspects, and a semiological analysis of food will apply a denotative, structured analysis to describe food objectively, and as a point of departure for analysing the social significances of food. Such an analysis can be organised according to the cultural sign model (Figure 4.4), from which pertinent fragments are selected, and expanded in the plane of expression, according to a series of contrasts and differences. Such analysis might take inspiration from Lévi-Strauss (1966), by exploring food as a structured system, and the experience of food at the level of the senses, and the way the experience is motivated by cultural factors (see Hawkes, 1977, p. 53). This brief excursion into food is merely a demonstration of what might be done, and it illustrates that cultural analysis is unlimited unless particular questions are posed. Food understood as a signifying system has an “alimentary language” made up of rules of exclusion or taboos, signifying oppositions such as savoury/sweet, rules of food association in the composition of menus and dishes, and rituals for eating that function as “alimentary rhetoric” (Barthes, 1964/1973, p. 28). Semiological analysis will reveal the way coded conventions govern food combinations, how food is cooked and presented in cultures, at individual, social and institutional levels. Food as phenomenon (matter or purport) can be ordered to 113 substantive disciplinary contents by fields such as physics, biochemistry, and anthropology (substance of the content) (Figure 4.9). Anthropology for example will focus less on food in terms of functional survival, than for its exchange values, social and symbolic sign values.

Food phenomenon

Physics, Biochemistry Anthropology

S Physical, functional, economic, social, symbolic C F E F Morphology, configuration, arrangements S biochemical, culinary, aesthetic etc.

Food as matter, material substance

Purport/matter/continuum

Figure 4.9. Food as cultural system.

At the denotative level food has functional significance for all cultures but the connotations of food vary between cultures based on rituals, beliefs, and religions, and on pragmatic considerations such as wealth, climate, and terrain (forms of content). At the same time food is matter (substance of the expression) that can be analysed for its physical and aesthetic characteristics, in each case into a variety of morphological characteristics and compositions (forms of expression). The form and substance of the expression “food” can be subjected to exhaustive description in the plane of expression, into the expression line or syntagmatic chain of combinations and the expression side or paradigmatic side of associations. Significant points of invariance will be identified in the signifying matrices of expression lines by commutation, which, by association, enables food elements to be organised into categories for classification. This technique is fundamental in the natural sciences and the analysis of food, its division and classification will reveal different types and combinations of proteins, fats, and carbohydrates; and the elements of these components will in turn enter into different combinations (Mathews, & Van Holde, 2000). Protein structure, for example, at the primary level is composed of a syntagmatic chain of amino acids 114 ordered specifically for each different protein according to the genetic code. Specific genes occurring at significant positions in the genome are responsible for the order of amino acids in proteins. A change in the order of genetic material (nucleotide bases) as an error, leads to a change in the primary structure of proteins. For example there may be a substitution of one amino acid for another or a deletion of an amino acid leading to a different protein. This may have no significant effect or may result in a mutation and a faulty protein (Mathews, & van Holde, 2000). The task of uncovering the systematic relations of proteins belongs to the specialist disciplines biochemistry and molecular biology (substance of the content). The meanings of the structure and function of food in this manner are given as first order denotative statements in biochemistry. They have been verified by extensive experimentation, and conform to the internally coherent rules and procedures set down by the community of biochemists (Figure 4.7, Level II). Schwab (1964b) refers to the elaboration of scientific statements as the “syntax of stable enquiry”, which is generally understood in terms of the sequence, observation, hypothesis, data collection and experiment, conclusion or scientific statement or proposition (pp. 31-32). The scientific statement is not the province of semiology, which provides merely a metalanguage for describing these scientific processes in the linguistic register (Hjelmslev, 1970, p. 132). The biochemistry discipline tries to understand food at the denotative level in terms of chemical structure and function. Socio-semiotics or material semiotics is concerned with the forms and substances of food as an anthropological phenomenon, that enters into discursive relations in the plane of content, historically, geographically, politically, economically, psychologically, and socially (forms of the content). Food as matter can be subjected to formal analysis in the plane of expression and formed in many ways through cuisine, and the culinary activities revealed in recipes, menus and dishes (Barthes, 1964/1973, p. 28). Syntagmatic units or pertinent fragments such as recipe ingredients, dishes and menus, can be analysed into component elements co-existing in relations of combination. These elements will in turn be reduced into significant units at points of invariance identified in signifying matrices, which are mutually correlated, by commutation, with elements in the plane of content. Associations with food will arise at significant points of invariance in recipe ingredients, meal menus, and the order of courses. These paradigmatic or associative relations can be categorised and classified with the 115 expectation that codified cultural content will emerge, that reveals similarities and differences between social groups. There are conventions that govern the cooking of food and food combinations and these are to a degree, complex, coded sets of relationships that provide insights into the workings of culture (Hawkes, 1977, p. 53). Barthes’ simple bread example explains how the signifying matrix works. Bread, the object of signification, has a life sustaining function, but there are invariants in bread forms that signify different social circumstances. The conceptual opposition bread without crusts versus wholemeal bread signifies the conceptual opposition, formal reception versus wholefood kitchen, crust being the support for the signification (Barthes, 1967/1990, p. 66). The level of formal structures, the plane of expression, gives only part of the story, food as system of signification. Semiological analysis will also demonstrate that food enters into human interactions in taboos, rituals, superstitions, and traditions (Lévi-Strauss, 1966). The rhetoric, ideology or social values communicated by food, is examined in an unstructured way through the mechanism of connotation (Barthes, 1964/1973; Hjelmslev, 1943/1961). The experience of food is not just functional or social, it is also aesthetic, and values are communicated in the presentation of food in the same way as fashion, and food as commodity is subjected to the same logo-techniques of packaging, advertising and marketing. There is unlimited scope in the analysis of phenomena such as food at the level of connotation, and the analysis depends on the creativity of the analyst, “operating” on the food phenomenon in response to specific questions. This kind of creativity is demonstrated in Baudrillard’s analysis of household objects (1968/1996), as follows.

4.2.4.2 The connotations of household objects

In The System of Objects Baudrillard (1968/1996) tracks the process of cultural change through consumers’ experiences of traditional, industrial and modernist interior designs, household furnishings and everyday household objects. As Gottdiener (1995) explains, this study illustrates the shift to the “commodification” of cultural objects, as an effect of cultural change under the “sign of modernity” (p. 40). Baudrillard’s interest lies less with the functional structural analysis of objects than with the way people relate to objects (1968/1996, p. 4). As a system of signification, interior design practice is analysed not for functions but for 116 affects, illustrated in the atmospheric values expressed by the use of colour for example (p. 30). As a system of communication, interior design practice is analysed at the connotative level for its socio-ideological implications (p. 109). Baudrillard begins by speculating on the possibility of classifying household objects in a manner analogous to the way flora and fauna have been classified in natural history (p. 3). He notes that technological objects have previously been defined according to functions and operations, by their subjection to formal, functional, structural analysis, and documenting the changes in social structures associated with technological progress (e.g. Giedion, 1948) (Baudrillard, 1968/1996, p. 4). To subject objects to systematic analysis using semiology, it will be acknowledged that technological systems do not have the same sort of structural autonomy as language (p. 11). They cannot be reduced to distinctive units like the second articulation of language (figurae or phonemes). Structural analysis will however isolate invariant elements or minimal meaningful units, “technemes”, which by comparison to similar but different elements in the technological system, will provide a means for clarification of the functional progress that occurs through technologies (p. 7) (laboratory examples are demonstrated in Sections 6.3 and 7.2.2). There are structural constraints in technical arenas such as laboratories, driven as they are by functional imperatives, which severely limit connotative effects (Baudrillard, 1968/1996, p. 7). Technological objects as systems of signification will be however, subject to the same sorts of constraints identified by Barthes for the fashion system (1967/1990), although to more subtle effect. As Barthes’ asserts, there is no object that does not communicate meanings other than function (1984/1988, p. 182). Wherever there is culture, functional objects carry sign values as well as use values and exchange values (Barthes, 1964/1973, p. 41) and this is applicable to laboratory objects as much as any other commodity. Baudrillard (1968/1996) bypasses the straightforward analysis of technological objects that would be applicable to technical arenas such as laboratories, aeronautics, astronautics and shipbuilding (p. 7). His purpose is to explore not the structures of objects but the way objects are experienced, “what needs other than functional ones they answer, what mental structures are interwoven with and contradict their functional structures”, and the cultural systems that underpin the experience (p. 4). This system of meanings will not be readily revealed by analysis of a technical system at the objective level, by dividing and classifying objects into categories and functional 117 systems. The experience of objects can be explored in systems of behaviour and interpersonal relationships that cannot be apprehended at the practical level (Baudrillard, 1968/1996, p. 7). The psychological and sociological reality of objects is superimposed upon their perceived materiality and functionality through connotations that continually modify the technological system and create a “disturbance” (p. 8). It is this disturbance that Baudrillard aims to explore, “the way the rationality of objects comes to grips with the irrationality of needs”, and in this contradictory situation, direct experiences of objects continually emerge (p. 8). Thus practical objects are not just structured, they are “in perpetual flight from technical structure towards their secondary meanings, from the technological system towards a cultural system” (p.8). The consumer/user of technical objects plays a role in this process according to his/her individual needs and through social interactions in the context of use, so that technological objects as elements in a system of objects, are as much dependent on social conditions of use and the global order of production and consumption, as they are on material structure and function. Because “techniques are always checked by practices”, Baudrillard argues that no system of objects has been described scientifically unless the “continual intrusion of a system of practices into a system of techniques” is also addressed, and this will include critique of the belief systems or ideologies that technological systems inadvertently express (p. 10). Variations of the socio-cultural models applied by Barthes, Baudrillard and others are explored in the next section, as they are applied to educational settings including classrooms and laboratories.

4.2.4.3 Semiology, material semiotics and education

Semiology is useful for analysis of two broad aspects of education, the structure of the knowledge base of disciplines and their location in Discourses and contexts, Discourses being understood as transdisciplinary Mode 2 knowledge systems (as discussed in Section 3.2). Such analysis will benefit from other ideas, theories and models. The substantive structures of disciplines can be approached taking guidance from Schwab’s structure of scientific disciplines (1964b), Foucault’s archaeological method (1969/1972), and Hjelmslev’s cultural sign model (1943/1961). The details of structures require working with pertinent fragments in the plane of content or the plane of expression (as discussed in Sections 4.2.2 & 118

4.2.3.2). Scientific textbooks do not usually provide these kinds of structures, but give lists of topics and detailed explanations of theories, propositions, and experimental procedures (e.g. Burtis, & Ashwood, 1999). This omission is addressed in the structure of clinical chemistry knowledge provided in Section 6.2. The structure of a discipline begins with the cultural sign model (Figure 4.4), and the ordering of disciplines around a phenomenon of interest, to a substantive content (substance of the content) and its various formations (form of the content). Thus a phenomenon (purport or matter), for example light, sound or health, will be ordered to a substance, a specific field of inquiry such as physics or medical science, and be represented in various ways and have a material basis or technological application (form and substance of the expression) (see Figures 6.1 & 7.2a). As Schwab explains (1964b), there is no way to describe substantive structure in general, but a few general principles are broadly applicable to the conceptual structures of scientific disciplines. These are the structuralist principles, division, classification and system (p. 46). The sciences are particularly concerned with testing the propositions and verifications of statements arising from these structured activities (Figure 4.7, Level II). Schwab (1964b) refers to this as the method of “stable inquiry” or “short term syntax” of a discipline, and although the order is varied in different sciences, it is commonly understood in terms of observation, hypothesis, experiment, data collection and analysis, hypothesis acceptance or rejection, explanation, and prediction (pp. 31-39). A scientific discipline will thus be structured as a network of interrelated theories, propositions and statements, their technological applications and mathematical representations. Ordering the fields and disciplines in this manner also provides a basis for their comparison, thus enhancing understanding of different modes of inquiry (Schwab, 1962, 1964a, 1964b). The “long term syntax” of scientific disciplines is a different matter, rendering stable inquiry problematic, exposing disparities and contradictions, external economic, political and social conditions, values and vested interests that motivate different forms of scientific inquiry (p. 39). Foucault’s archaeological method is also useful for analysis of these structured and dispersed aspects of Discourses, and also for understanding constraints in different contexts (see Section 6.2). One of the main purposes of performing a socio-semiotics analysis of a context, incorporating material semiotics, spatial analysis and connotative semiotics, is to identify factors in the context that constrain participants’ experience to a 119 culturally coded or Discourse specific experience, underpinned by values and vested interests (Gottdiener, 1995). Spaces, buildings and objects can be subjected to analysis in the plane of expression (and/or the plane of content) from which invariants in spatial arrangements or points of significance, are used for comparative analysis with other spaces, based on similarities and differences. Laboratories and instruments can be “read” in this manner, objectively, as structured systems of signification with respect to their function, subjectively for what they communicate other than function to users, and at the socio-ideological level to gain insights into the values that underpin medical science Discourse. Two approaches to the analysis of spaces and objects in educational laboratory settings, one of which is semiotic, illustrate what spatial analysis can reveal. The first analysis, conducted by Arzi (1998), provides a historical analysis of the evolution of school laboratories, their structures and functions and relations with the goals of laboratory classes, the aim being to understand how the physical environment exerts its effect (p. 599). Retrospective longitudinal analysis of laboratories exposes the interplay between laboratory structure and educational function. In analytical chemistry for example, increasing sophistication of laboratory instruments has led to a change in laboratory structure, leading to the replacement of large reagent benches with more open designs and greater potential for classroom interactions. The objective of historical analysis of this kind is to stimulate projections of future oriented laboratory designs that are flexible and multi- functional (p. 601). The second analysis, conducted by Shapiro (1998), provides a semiotic understanding of science learning environments, and of how students and teachers might work together to develop scientific knowledge, skills and attitudes. It is assumed that science culture provides “a set of signs, symbols, and rules” which can be used to “create and ‘read’ the learning environment” (p. 609). In this approach the furniture “speaks” about science culture in the classroom, and communicates more than science content to students (p. 609). The classroom furniture can be read as a text, as part of an elaborate system of signification and communication, because the layout, the arrangement of chairs, the placement of objects, all communicate the way a class will be conducted, and power and authority differentials (pp. 610-612). The social semiotic approach seeks understanding of the laboratory at the material level (form and substance of the expression) and also for the attitudes and ideologies 120 connoted about the positions of students and teachers at individual and institutional levels (forms of content). Connections are thus made between the formal structures of classrooms as signification systems, and many “constellations of meaning” in educational institutions and the wider society (p. 611) (see also Bernstein, 2000). Shapiro (1998) aims to alert science educators to the idea that students are expected to understand and use many sign systems at once, and that learners who can interpret, and read signs have a distinct advantage over those who do not. Problems arise Shapiro argues, when sign givers (teachers) and sign receivers (students) do not share the same codes (p. 618). Cobern (1991) proposes research into worldviews in science classrooms in recognition of this problem. Bernstein (2000) investigates similar issues using socio-linguistics, to analyse the social structures that underpin pedagogic Discourse, the codes of pedagogic transmission, and the social and institutional regulatory Discourses that guide the transmission as forms of symbolic control. This regulatory Discourse is revealed in the “framing effects” that illustrate the “locus of control” between teacher and student over for example, the selection and sequencing of lesson content, and the pacing of learning (p. 99). Regulatory Discourse in educational practice is exemplified in a study superimposing codes of school dress on the system of institutional rules (Symes, & Meadmore, 1996). Textual analysis of school prospectuses, handbooks and promotional materials uncovers “a whole system of educational tactics centred on subjectification”, the regulation of minds, bodies and behaviour in schools (p. 174). “Corporeal practices” are revealed by integrating the analysis of both bodily practices captured in the analysis of clothes and dress, the signifiers of appearance (following Barthes), and the forms of socialisation that tie dress codes in with broader historical forces (following Foucault) (Symes, & Meadmore, 1996, pp. 171-173).

4.2.5 Socio-semiotics applied to clinical chemistry Discourse

From a socio-cultural perspective, two aspects of clinical chemistry (and medical science) Discourse are considered, the structure of the knowledge base, and the laboratory context including the tools or instruments of laboratory practice, for the constraints they impose on knowledge workers’ experience. The structure of clinical chemistry knowledge is addressed in Section 6.2, by placing it within the substantive content of medical science knowledge and its juxtaposition with other 121 forms of health knowledge (Figures 4.4 & 6.1). Such analysis is useful for cross- disciplinary purposes because it raises awareness of other fields such as psychology, sociology, philosophy, anthropology and economics, without requiring the analyst to become a specialist in each field. This kind of awareness is useful for evaluating laboratory tests in EBLM. The structuralist principles, linguistic algebra, or analysis in the plane of expression (Sections 4.2.2 & 4.2.3.2), are applied in Section 6.3, to the teaching laboratory’s spatial arrangements in comparison with industry laboratories, in order to provide objective descriptions, and as the point of departure for connotative analysis seeking socio-ideological insights into knowledge workers’ experience. Spatial analysis applies similarly to laboratory instruments in Section 7.2.2, for the purpose of gaining insights into the logic of laboratory practice in Section 7.3.3, as explained by the sign triad of Pierce (1931-58) in Section 4.3. As a conclusion to this section, the point is reiterated that cultural analysis using semiology draws on structuralist principles to guide the analyst towards matters of social significance. Such analysis is reliant on the creativity of the analyst, and often the method itself is obscured. In order to give structure to this kind of analysis, the sign triad and logic are needed.

4.3 Logic and pragmatics in scientific practice

There are strategies inherent in the structure of Discourses built up from the dyadic sign model (signifier-signified, expression-content, representation-idea- relations) explained in Section 4.2, that connect with the triadic sign model (representation-object-interpretation), used in this section to explain logic and pragmatics in scientific practice. The purpose of this section is to synthesise a relatively simplified framework from a complex set of theoretical propositions about the way cultures and Discourses function in signs, for application to clinical chemistry laboratory practice. Several features of knowledge work, incorporating symbolic analysis, are addressed by this framework: logic in the rule-governed performance of methods; logic in method selection in different analytical circumstances, and in error detection and diagnosis; pragmatic modification of rule- governed procedures due to circumstantial constraints such as budgets; recognition of conflicting values in the interpretation of information; and detection of “framing effects” that bias the way information is interpreted in communicative situations. 122

This range of features for knowledge work encompasses “D” competence in a Discourse at two critical levels, as defined by literacy theory (Section 3.5.1). “D” competence in a discipline ensures valid interpretations are made, and innovations and improvements; and for “D” competence of Discourses socially critical perspectives are added to ensure that conflicting values are considered. Both forms of competence are reliant on the ability to manipulate and interpret representations, and that ability is captured by a tacit integration in the work situation, of representations, objects and interpretations, the sign triad of Peirce (1931-58) (as introduced in Section 3.5.2). The purpose of Section 4.3 is to explain how the triadic sign model of logic can be used as a basis for improving the visibility of knowledge work and symbolic analysis in computerised environments, and in the production of socially accountable knowledge. Several stages are required for this purpose. The triadic sign model and logic as derived from Peirce (1931-58) is explained as pragmatic, grounded in the basic principle that logic is semiotic, sign action or semiosis, reasoning from sign to sign with a pragmatic purpose in mind. Sign logic is specified for different purposes, deduction, induction, and abduction or hypothesis, and for defining modes of Discourse, scientific and unscientific (Morris, 1971). The dimensions of semiotics, structure, logic and rhetoric, are converted for methodological purposes, into syntactics (relations among signs), semantics (relations between signs and their objects) and pragmatics (relations between signs and interpreters) (Morris, 1971, p. 21). Discourses are distinguished according to the different ways they operate with sign systems to accomplish particular purposes (p. 203). The three dimensions of semiotics, syntactics, semantics and pragmatics, are synthesised within one semiotic model (Eco, 1976). In this synthesised model, signs as dyads and signs as triads are integrated, so that cultures are structured by codes that govern behaviour, but the same model demonstrates that codes get modified as they are used. Points of commonality and difference between the dyadic and triadic sign models are noted in discussions of the triadic sign model. This is in order to provide clarification of both approaches, of the purport or continuum described by Hjelmslev (Section 4.2.2) which bears close relationship with the dynamic object of Peirce (Section 4.3.2); and the signifying matrix identified by Barthes (Section 4.2.3.2), which represents the threshold for signification or logic in the immediate object of the Peircean sign (Section 4.3.2). Both systems address scientific division, classification and system 123

(the plane of expression) (Section 4.2.2 & 4.2.3.2). Before demonstrating how the synthesised model works, the epistemological assumptions of the model are considered.

4.3.1 A semiotic model of Discourse and culture

This section examines the boundaries of the semiotic approach that integrates sign dyads and sign triads, and the assumptions on which the approach is based. In order to integrate structures and use, Eco (1976) proposes a dual theory, a “theory of codes” to account for the codified aspects of cultures, represented in structured sign systems (as described in Section 4.2); and a “theory of sign production” (logic and pragmatics) to account for the way codes are created, modified and used. The semiotic approach can be described as “scientific” given the propensity to define structures based on cultural codes and rules. It is not scientific in the same sense as activities are in the natural sciences, aimed at the verification of statements, explanations and predictions derived from observations, hypotheses and experiments. Semiotics is not verifiable as such, and is concerned rather with “social forces” for which plausibility and social utility is sought (Eco, 1976, p. 65). There are two broad approaches to semiotic inquiry, inductive and deductive, as Hjelmslev explained for the language system (Section 4.2.2). Taking the inductive approach, semiotics will be concerned with systems in culture, induced from empirical observations of sign phenomena, in a similar manner to the way anthropology has proposed kinships systems, totems and myths to explain mechanisms of culture (Lévi-Strauss, 1966); and structural linguistics has explained the underlying systematic basis of natural language (Saussure, 1959). Taking the deductive approach, semiotics as a discipline provides the principles and method for analysing the sign systems of culture deductively, by drawing on pertinent data, excluding irrelevant data, and making deductive inferences about culture (Eco, 1976, pp. 7-8). The deductive approach is of interest in this section. In code theory, Eco (1976) proposes a semiotic model, a “supposed cultural structure”, which is organised into semantic fields, independent of what happens in the minds of addressees (p. 83). Code theory applied to disciplines will thus be concerned with what can be known, rather than how it comes to be known, not who says what to whom in communication, and in what way, but what can be said in a 124 given language, what is available for circulation (the knowledge base). Code theory is based on the assumption that there is a universe of possible significations that must be consulted before communicative processes in cultures can take place (Eco, 1976, p. 4). A signification system is defined as “an autonomous semiotic construct” with an abstract mode of existence independent of communicative acts, each act of communication presupposing a system of signification, in which material expressions and contents are matched (p. 9). Codes are the underlying cultural rules needed to ensure that what is presented to the perception of an addressee, a representation, “actually stands for something else”, a concept or idea, in a correlation that is “valid for every possible addressee” whether or not the addressee is present (p. 8). Structured significations are thus “culturally recognized and systematically coded”, and a sign is present every time a human group decides to use and to recognize something as the vehicle of something else (p. 17). In the medical world for example, symptoms such as red spots by inference become signs of disease such as measles, but once that constant relationship has been demonstrated, the connection becomes culturally accepted and registered as a medical semiotic convention (p. 17). The segmentation of the world into semantic fields composed of expression-content relations also reveals the world vision of a culture (p. 76). Codes bind people together in “a set of cultural conventions” or rule-based structures accepted by the society which sets up its “cultural world” (p. 61). Codes are the underlying mechanisms of a cultural order, the way a “society thinks, speaks, and while speaking, explains the ‘purport’ of its thoughts through other thoughts” (p. 61). These are the processes through which a society develops, expands and collapses, and code theory is concerned with the “format of such ‘cultural’ worlds” (p. 62). The “continuum” (matter or purport) of a cultural phenomenon is divided not in arbitrary fashion, but according to the experience of each culture. The same continuum is divided differently by different cultures (form and substance of expression and content), and their comparison enhances our understanding about cultures (p. 79) (Figure 4.4). Within the same culture however cultural units acquire many interpretations, thus introducing the problem of bias (p. 80). Once culture is accepted as a semiotic phenomenon it can be understood as a system of cultural sign units (expression-content relations) given structure in semantic fields. Things are known from a semiotic perspective through cultural units which “the universe of communication puts into circulation in place of things” using 125 words, drawings and any other representative means (Eco, 1976, p. 66). Every aspect of culture is a semantic or cultural unit and anything that can be culturally defined and distinguished including objects, persons, places, behaviours, feelings, gestures and ideas can be clarified in these terms (p. 67). A cultural unit is inserted into a system of expressions (representations) at the syntactic level (plane of expression), which the code ensures corresponds to certain cultural contents at the semantic level (plane of content). A cultural world can be represented as a vast network of cultural units (expression-content relations) interconnecting in a semantic universe structured by culture into “sub-systems, fields, and axes” (p. 75). At the semantic level, codes based on dyadic sign units define the expression- content connections pertinent in a culture. For the purpose of logic and pragmatics, interpretation is considered in terms of the mediation between representations, the object world and interpretations, based on the sign triad (Peirce, 1931-58). The triadic sign is dynamic, such that each sign becomes a representation for another sign, in the process Eco (1976) refers to as unlimited semiosis (p. 71). There are limitless possibilities for interpreting the semantic universe pertaining to an unstructured phenomenon (purport, continuum). These phenomena are given structure in global semantic systems defined by disciplines and their various formations (substance and form of the content), but these too are subject to unlimited interpretations. In order to do useful knowledge work, it is necessary to operate at local levels in pertinent semantic fragments. Effective navigation of local fragments (knowledge work) entails making logical rule-governed connections, pragmatic contextual and circumstantial selections, and interpretive choices drawn from alternative, sometimes complementary, sometimes contradictory semantic connections (p. 81). A function of semiotics is social criticism which requires recognition of the connections representing alternative world visions, because in communicative situations, codes can be manipulated, accidentally, deliberately, subconsciously, or surreptitiously (p. 289). Rhetorical discourses such as the visual arts make use of such strategies deliberately in order to challenge the existing social and aesthetic orders (p. 261). Inappropriate ideological bias is surreptitious, contradictory readings are hidden in order to manipulate audiences, often to favour the concerns of those with vested interests in particular interpretations (p. 289). Rhetoric, the art of persuasion, is unavoidable in communication, but debate can be 126 honestly persuasive if contradictory interpretive choices are given recognition. Semiotics is thus a model for social criticism and social practice (pp. 294-298). Competence in a Discourse can be defined within a semiotic framework of a Discourse, which is constructed from pertinent fragments of knowledge, built up from Discourse units (expression-content relations), participating in a plane of expression mutually correlated with a plane of contents (at least in the sciences) (Guiraud, 1975). Semantic content units are placed in a system of positions (contexts) and conceptual oppositions (circumstances), denotations and connotations, drawn from the global semantic system represented in a fragment (refer to Figure 4.14). “Designative informative” natural science Discourses attempt to limit interpretive choices and to stabilise meanings derived by consensus in the science community (Morris, 1971, p. 206-208). In such Discourse, as Eco (1976) explains, a robot would possess an “assortment of semantic fields and rules to link them to systems of sign-vehicles” (expressions) (p. 83). Artificial Intelligence (AI) and Expert Systems are supplied in this way with a knowledge base, and a network of logical rules of connection (inference engines) (see also Chi et al., 1988; Gillies, 1996; Jackson, 1999). Humans, robots or AI are expected to behave with “ideal” competence in a rule-governed way, based on the semantic or intended meanings of a Discourse (Eco, 1990, pp. 46-52). Humans have additional capacities over robots, in that they can make pragmatic decisions; ethical and aesthetic value judgements; can extract meanings from iconic forms such as pictures, diagrams, graphs and charts, as well as from abstract symbols and numbers; and can recognise the rhetorical strategies used to manipulate communicative situations (Eco, 1976). This range of capacities applies to “D” competence for knowledge work in transdisciplinary socially accountable scientific knowledge systems. Three theoretical constructs converge on this ideal of knowledge work, “model reader theory” (Eco, 1990; Olson, 1994); dictionary and encyclopaedic representations of competence (Eco, 1984); and “D” competence in Discourse theory (Gee, 1996). Firstly, there are two model readers and two levels of interpretation, semantic and critical (Eco, 1990, pp. 52, 54). Every text is susceptible to semantic, rule-based or culturally coded, and critical interpretation. Critical interpretation is a deliberate and meta-linguistic activity aimed at describing and explaining why a text elicits a certain response (p. 54). In other words the way things are represented, written or spoken is as important as what is represented, written or spoken. Not every 127 text aims to elicit the second critical response. A scientific text for example aims to denote a certain message, verified in an elaborate set of scientific procedures, and every attempt is made to stabilize interpretations to those intended by the author. Alternatively, artistic texts, novels, films, sculptures, paintings and installations, aim to destabilise meanings, provoke responses and discussions in order to challenge and disrupt the status quo. The second theoretical model of competence is defined in terms of an opposition Eco (1976, 1984, 1990) sets up between dictionary and encyclopaedic competence with knowledge. Dictionary competence requires command of culturally coded factual statements, rules, laws and principles, whereas encyclopaedic competence require interpretation of knowledge as it is represented in structures, expressions and contents, linked in an infinite variety of ways in different contexts and circumstances. The competence of a social group cannot, however, be posited as the “vast range of all possible knowledge” of potential coded correlations. Such a model is not particularly useful, being unwieldy, and unpredictable, composed as it is of an infinite unordered set of markers (denotations and connotations, contexts and circumstances) (Eco, 1976, pp. 96-99). It is necessary to operate with local fragments for which dictionary knowledge is useful precisely because it is coded, and can ensure the validity of interpretations. “Local dictionary” knowledge is presupposed whenever “we want to recognize and to circumscribe an area of consensus within which a given discourse should stay”, that is local cultural knowledge, discipline or Discourse specific (Eco, 1984, pp. 84-86). The dictionary is a register of factual statements and in providing coded representations its saves “definitional energies” in a Discourse context in which validated principles are used, and presuppositions and assumptions are taken for granted. In other words, criticism in disciplines precludes criticism of Discourses. The third theoretical model of competence from Discourse theory (Gee, 1996; Lankshear, 2000), places competence on a continuum between “d” and “D”, for operational, cultural, and critical competence in and of Discourses (see Section 3.5.1). The first model reader with operational and cultural competence at the semantic level is critical in the disciplines, ensuring their constant evaluation, optimisation and revision. The second model reader is critical of Discourses, and recognises that knowledge is underpinned by values and framed in certain ways to 128 elicit interpretations compatible with the world view the Discourse represents, or to suit those with vested interests in particular interpretations being made. The semiotic model of interpretation for knowledge work in Discourses is built upon the sign triad in the next section. Owing to the sheer volume and complexity of the material reviewed, it is necessary to state that any injustice perpetrated against the theorists used for this purpose, principally, Peirce, Morris and Eco, is inadvertent, and will hopefully be compensated for in the demonstrations of the power of semiotics to illuminate laboratory practice in subsequent chapters.

4.3.2 The sign as triad

The sign triad (representation-object-interpretation relation) was introduced by Charles Sanders Peirce, who was a principle founder of the American movement “pragmatism”, also associated with William James, George Herbert Mead and John Dewey (Peirce, 1931-58, 5.1) (note that hereafter all references to Peirce derived from the eight volumes, 1931-58, are given by the convention, volume and paragraph, not page number). It is a function of pragmatism, according to Peirce, to “lay down a method of determining the meanings of intellectual concepts” to explain thinking and reasoning, without trying to say “in what the meanings of all signs consist” (5.8). Peirce provides “An outline classification of the sciences”, that accounts for mathematics and philosophy, which in turn includes phenomenology, metaphysics, and the normative sciences, ethics, logic (semiotic) and aesthetics, and the special sciences, classified under the headings physical and psychical science (1.180). Logic is semiotic, “the quasi-necessary or formal doctrine of signs” used to explain “‘scientific’ intelligence”, that is “intelligence capable of learning by experience” (2.227). Scientific activities are placed along a pathway between “genuine doubt” and “fixed belief”, and lead to conventions or habits (Peirce, 1877). Smythe and Chow (1998) clarify Peirce’s use of the word habit as “flexible, self- modifying and controlled” by conscious intention, rather than the “conditioned reflex” that might be expected of the word habit (p. 791). Scientific habits are based in logic which is reducible to actions with signs, semiosis, integrating representations, object reality, and interpretations in an infinite progression. Scientific experience can be explained by mapping local fragments of the semantic universe as chains of signs in the process of unlimited semiosis, which accounts for 129 logical scientific, pragmatic, rhetorical and ideological interpretations (Eco, 1976, p. 69). The three dimensions of semiotics, syntactics, semantics and pragmatics (Morris, 1971), derived from the ancient trivium, grammar, logic and rhetoric by Peirce (1931-58, 2.229), are thus integrated for methodological purposes. The dynamic triadic sign model mediates between knowing and doing, structure and experience. It brings that which is known into contact with the object world of experience. The sign triad accounts for the “structure of tacit knowing” (Section 3.5.2). The relation between understanding or knowing a comprehensive object and mastery of a skill or doing is improved by its “dismemberment” or division into parts and their re-integration, made possible by visual perception or perceptual recognition (Polanyi, 1969, pp. 123-126). The “triad of tacit knowing” (or the Peircean triad) consists in “subsidiary things B bearing on a focus C by virtue of an integration performed by a person” (pp. 181-182). Thus, “A stands for B to C” and for thing B to have bearing on object C it endows it with meaning (p. 181). Subsidiary things that bear on a focus are representations of qualities, things, ideas, feelings, facts, events, and laws, and stand for the object of a sign (anything that is represented) that creates an effect on a person (interpretant). In order to mediate thinking, an interpretant must in turn be represented by another sign in a process that is infinitely expandable. As Peirce (1931-58) explains, “whenever we think we have present to the consciousness some feeling, image, conception, or other representation, which serves as a sign” (5.283), but to think is also to connect signs together so that “each former thought suggests something to the thought which follows it” (5.284). Thinking understood in terms of the semiotic sign is dynamic and triadic, requiring the integration of a representation (R) of a thing (O) (concept, gesture, feeling, place etc.) as it is given to perception, and a sign in the mind or thought about the thing, interpretant (I), which is in turn represented in another sign.

A sign, or representamen, is something which stands to somebody for something in some respect or capacity. It addresses somebody, that is, creates in the mind of that person an equivalent sign, or perhaps a more developed sign. That sign which it creates I call the interpretant of the first sign. The sign stands for something, its object. It stands for that object, not in all respects, but in reference to a sort of idea, which I have sometimes called the ground of the representamen (2.228).

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In thinking, sign action or semiosis, something, an interpretant (I), takes something else, an object (O), mediately by means of a third something, a representamen (R) (expression, signifier, sign vehicle or representation). A representation is not to be confused with the sign itself, because “Sign and Explanation together make up another Sign” requiring an additional explanation and so on (2.230). Thoughts cannot be separated from further thoughts so that thinking is dynamic, represented as a continuous network of related signs, the interpretant each time becomes the representation for another sign. The sign/interpretant is thus the medium through which “the torch of truth is handed along” (1.339). The triadic sign is first represented in simplified form as a semiotic triangle (attributed to Ogden, & Richards, 1923; see also Eco, 1976, p. 59) (Figure 4.10a), a representation (R) stands in triadic relation with the object (O) of the sign and the first effect of the sign, the interpretant (I), on the interpreter of the sign. This triangle does not however, distinguish between reference (object as specified in context) and referent (actual object), nor indicate the sign’s dynamic nature (Eco, 1976, p. 58).

Interpretant Final I I

R 4 I 3 R3 I2

R2 I1 R O Representamen Object R1 O

Figure 4.10a. The sign as triad. Figure 4.10b. Interpretation.

Signs refer not to objects per se, but to the cultural content of the object in context (reference). The term “dog” for example, refers not to the actual four legged creature, but to “all dogs” as a set, class or logical entity, to all possible cultural interpretations about dogs, and this will include everything known and things as yet to be known (Eco, 1976, p. 66). In order to establish what the interpretant of a sign is, it must be named by another sign (p. 68). The triadic sign model has an internal dynamic logic, so that representations, objects and interpretants, are continually modified through the chain of interpretants. Figure 4.10b represents the “progression 131 of interpretants” (adapted from Smyth, & Chow, 1998, p. 790) illustrating that for the interpretant (I1) of the first representation (R1) to participate in interpretation, itself becomes a representation (R2) of the interpretant that follows (I2), which is in turn a representation (R3) of the interpretant (I3) that follows, and so on in a potentially infinite series. I and R are thus interchangeable terms. There are three kinds of interpretants, the immediate one given, the dynamic interpretant participating in a progression, and the final interpretant that brings the progression to closure in yielding a result. Figure 4.10b does not account for the object of the sign of which there are two, the immediate object as it is represented, and the dynamic object which accounts for external influences on the sign, context and circumstance (Eco, 1976, p. 69; Pierce, 1931-58, 2.230) (see also Houser, 1987; Smythe, & Chow, 1998). The object of a representation is also “a representation of which the first representation is the interpretant”, in an endless series of representations which are conceived to have an absolute object at a contextual limit. The final interpretant at the conceptual limit becomes established as law, habit or convention (Eco, 1976, p. 69) (Figure 4.10c).

O1

O I1 /R2 2 O R1 I/R O O

I/R I/R I/R O

I/R O I /R O 2 3 I/R I/R I/R O I/R O O

Figure. 4.10c. Unlimited semiosis (adapted from Cobley, & Jansz, 1997).

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The sign triad is thus geared towards a dynamic object and final interpretant, analogous to Hjelmslev’s purport/matter/continuum (Eco, 1984, p. 45) (Figure 4.4). The immediate object is the concern of semiotics and as part of the system of signs is itself a sign, a prior representation of the object of another sign. For the object of a sign to be specified it is first represented, perceptual presentations of immediate objects are given in experience prior to the acts of recognition, for example imprints as interpreted by the tracker, symptoms as interpreted by the diagnostician, and clues as interpreted in crime detection (Eco, 1976; Smythe, & Chow, 1998). Signs are thus functionally characterised as interpreted, interpreting and pre-interpretive entities, and the functions of R, I and O are interchangeable (Smythe, & Chow, 1998). The immediate object, the pre-interpretive aspect brings the external world into the account, and can thus be added to the Saussurean model which accounts only for signified (I) and signifying (R) aspects (see Figure 4.3). As Eco (1976) explains, the dynamic sign model characterises signification in terms of “continual shiftings” by which signs are referred back to other signs or strings of signs (p. 71). The metaphor “rhizomatic” (attributed to Deleuze and Guattari by Eco, 1984, p. 81) characterises the dynamic model as a network of potential connections in the universe of semiosis (a rhizome is a tangle of bulbs and tubers with no hierarchical structure, no ending or beginning, all points are in some way connected). All cultural units are ultimately accessible through other cultural units, circumscribed in asymptotic fashion (Eco, 1976, p. 71) as Figure 4.10c implies. The dynamic sign model raises three problems that Eco (1976) addresses. The first problem is identified as the need to move beyond the immediate interpretant (coded equivalence, E ≡ C) and activate a chain of interpretants in unlimited semiosis, and this is accomplished using logic. The second problem is identified as the need to achieve closure and short-circuit this potentially unlimited process. This is accomplished by placing contextual and circumstantial constraints on localised pertinent fragments selected from the network of potential connections in the global semantic system. The third problem is identified as the need to define the different levels of competence used in knowledge work with fragments. The first “model reader” with operational and cultural competence in a Discourse will make culturally prescribed interpretations. The second “model reader” will critically evaluate the methods, and values of the Discourse itself; and values are captured in the way things are represented, written or spoken in representations, being rhetoric. The first issue 133 requires exploration of logical modes of reasoning (Section 4.3.3), and the second and third issues require understanding of the nature of sign systems and modes of Discourse (Sections 4.3.4 & 4.3.5).

4.3.3 Sign classification and modes of inference

Logic is concerned with formal and informal modes of reasoning (Black, 1952; Cohen, & Nagel, 1934; Harré, 1960). The semiotic perspective of logic is concerned with informal, everyday logic as it applies to scientific procedures, as opposed to formal logic in which scientific propositions and their interrelations are analysed in abstract symbolic expressions. Peirce (1931-58, 2.266-270) specifies the modes of reasoning used in informal logic using sign categories and sign combinations to explain three principle modes of inference, deduction, induction, and abduction/hypothesis. Deduction is necessary inference so that conclusions follow logically from premises. Induction is probable inference so that generalisations are made from observations of particular cases. Abduction/hypothesis is probable inference of the nature of things or events not directly observed, derived by informed guesswork. Logic is a very complicated matter (see Nickles, 1980, for an overview of the debates). This section provides an introduction to logic as semiotic, because it is useful for explaining knowledge work and symbolic analysis in fields such as clinical chemistry, in which knowledge workers must interact with diagnostic Expert Systems, which are programmed in scientific logic (Chi et al., 1988; Gillies, 1996; Sikaris, 2001). Peirce (1931-58, 2.243) divides all complex phenomena into threes or “trichotomies”. Signs are classified according to three trichotomies to incorporate the nature of signs, their relations to objects, and to interpreters, in terms of their potentiality, actuality and rule-governed regularity, thus producing nine sign types (Figure 4.11). The first trichotomy is concerned with the nature of the sign, as quality or tone (Qualisign) (e.g. a “feeling” of red, a sign of essence); actual thing, token, or event (Sinsign); or general type (Legisign) (e.g. laws as established by “men”) (2.244-246). The second trichotomy (2.247) is concerned with the relation of the sign to the object it represents, by resemblance as icon (2.276) (e.g. images and diagrams); by existential connection to its object as index (2.283) (e.g. a weathervane or pointing finger); or by an abstract arbitrary convention as symbol of which there 134 are many types (2.292). Signs thus resemble, point to, or stand in for other things. The third trichotomy (2.250) is concerned with the relation between the sign and the effect it produces in interpretation, as possibility (Rheme); actual fact (Dicent); or formal law (Argument). As Peirce (1931-58) explains, these sign types are abstract ideals and in any system of logical notation signs of several kinds will be used, and so it is not necessary to locate the sign precisely (2.265). These sign types however, provide the basic units from which Peirce draws ten classes of signs to characterise the common modes of reasoning or inference used in the sciences, the “trichotomy of arguments”, Deduction, Induction and Abduction/hypothesis (2.266). Shank (1998) and Cunningham (1998) seek a semiotic model for learning, and provide clarification of Peirces’ sign classes and their applications in everyday life (see also Shank & Cunningham, 1996). Before exploring these examples, the modes of inference, deduction, induction and abduction, are further explained.

Potentiality Actuality Regulation

Tone/quality Token/object/event Type/rule/law Nature of sign Qualisign Sinsign 3 Legisign

1 2 5 Sign-object relation Icon 4 Index 6 Symbol 10

Sign-interpretant Rheme Dicent Argument

relation Open Singular Formal

Mode of inference Abduction Induction Deduction

Abduction/hypothesis: 1. Hunch/omen (sign of future, anticipation, possible evidence) 2. Symptom (sign of present occurrence, actual evidence) 3. Metaphor (sign by analogy) 4. Clue (sign of past event, actual evidence) 5. Diagnosis (infer nature of error) 6. Explanation Induction: 7. Identification (description) (dicent, index, sinsign) 8. Prediction (dicent, index, legisign) 9. Model building (dicent, symbol, legisign) Deduction: 10. Formal reasoning

Figure. 4.11. Classification of signs and modes of inference.

According to Pierce (1931-58), logical forms called syllogisms (following Aristotle) are used to classify arguments, and although there are other ways to represent inference, there are three probable and approximate inferences used in the 135 sciences, “Deductions, Inductions and Hypotheses” (1.369). These three modes of reasoning are demonstrated in the three scenarios of logic Pierce applies to the selection of beans (2.619). The explanations are supported with Eco’s schematic representation (1984, p. 40) (Figure 4.12). Firstly, deduction is the application of general rules to specific cases, so that conclusions follow premises necessarily (rule → case → result) (Peirce, 1931-58, 2.619). For example, a handful of beans (case) is drawn from the bag of white beans (rule is all the beans in the bag are white), and it is inevitable that all the beans in the hand will be white (result).

Abduction Induction Deduction

Rule Rule Rule Proposition

Tentative proposition Case Case Case

Result Result Result

Figure 4.12. Modes of inference (adapted from Eco, 1984, p. 40).

Secondly, induction is the reverse process whereby a general rule is inferred probably, from the observation of the results of repeated cases (result → case → rule) (2.623). For example, a handful of beans is removed from a bag of beans of unknown colour on repeated occasions (cases) and on each occasion the beans are white (results), and the general rule is inferred that all the beans in the bag are probably white. Thirdly, abduction/hypothesis requires a more daring move. When a strange event (result) is encountered, a rule is consulted, sought or created and tested by experiment so that inferences are drawn about the nature of cases (rule → result → case) (2.623) (Eco’s version, result → rule → case, makes more sense) (Figure 4.12). For example, a handful of white beans is present (result), and a bag of white beans is also present (rule), and by abduction, the case is inferred that the beans are from the bag, but this is pure conjecture. The abductive approach shortens the tedious process of induction requiring many observations, and must be put to the test in 136 experiment (as for induction). Because abduction requires a rule to be created or consulted, its testing by experiment is a hypothetico-deductive procedure (Popper, 1972/1979). Experiments in the applied science laboratory require rule-governed deduction in ideal situations, induction for classifying objects, and abduction or informed guesswork applies in the detection and diagnosis of errors, once their signs, symptoms and clues are perceived in the experimental situation (as demonstrated in Section 7.3). Peirce (1931-58) draws a distinction between abduction and induction, which he claims are often confused (2.632-2.635). By induction we conclude that facts similar to observed facts are true in cases not examined, by reasoning from particular cases to general rules, in a classificatory procedure. By abduction/hypothesis we infer the existence of something different from anything observed (case), by observing a result to which is applied some known law or rule. This is reasoning from effect to cause not directly observable in order to provide a diagnosis or explanation of some strange event. Abduction or hypothesis is the bolder move, because it stretches induction beyond the limits of observation, so that inferences are drawn without direct observation. These distinctions are useful for classifying the sciences and discourses in general based on the different modes of reasoning they use (2.636). For example the classificatory sciences such as natural history, botany and zoology are systematic and inductive, whereas the theoretical sciences such as physics, biology and geology use hypothesis/abduction as well. Research scientists in some cases set out to discover a new rule using the abductive/hypothetico-deductive model in order to explain a strange phenomenon. Applied scientists (and Expert Systems) consult known rules deductively for pure science applications, and apply abductive reasoning in troubleshooting errors (as demonstrated in Section 7.3.3). Eco (1990) proposes that there are three modes of abduction, depending on whether a rule exists, exists but is hard to find, or does not exist and must be created, and uses these distinctions to explain differences between the natural sciences and the arts (p. 158). Peirce (1931-58) specified ten modes of inference based on mixtures of the nine sign types (2.264). Shank and Cunningham (1996) derive six modes of abduction based on the potentiality of signs, three modes of induction based on the actuality of signs, and one mode of deduction based on rule, law or regulation (see also Cunningham, 1998; Shank, 1998) (Figure 4.11). Shank (1998) refers to the six modes of abduction as “ground state cognition”, and argues that they 137 have great potential for instruction and learning (p. 841). The omen or hunch requires reasoning to a future possibility, as is used for example in weather forecasting and stockbroking; the symptom requires reasoning to something actual, existing in the present, used for example in diagnosing diseases and instrument malfunctions; the metaphor or analogy is used widely in all fields, for example in the sciences to represent and explain abstract ideas; the clue requires reasoning to concrete evidence of a past situation, used for example in criminal detection, in hunting or tracking, and in detection of laboratory errors; diagnosis/scenario requires reasoning to create plausible scenarios from a body of clues, and explanation requires reasoning to a general account. Troubleshooting errors in laboratory situations, based on recognition of symptoms and clues in instrument windows and graphic laboratory inscriptions is demonstrated in Section 7.3. It might also be demonstrated that laboratory managers can reason to the hunch in anticipating more efficient moves in laboratory organisations. In this light, the knowledge worker in clinical chemistry is simultaneously a symbolic analyst, detective, diagnostician and prognosticator or diviner. Expert Systems, comprised of large knowledge bases and inference engines, perform some of these reasoning tasks automatically (Chi et al., 1988; Gillies, 1996; Jackson, 1999; Sikaris, 2001). Human experts are superior in that they have acquired a vast knowledge base, accumulated through years of education and experience, and have the ability to short circuit the process of unlimited semiosis by isolating pertinent fragments from the global semantic system, making theoretical and pragmatic decisions (as described in Sections 4.3.5, 7.2.2 & 8.3). When cognition is understood as semiosis, learning is a matter of constructing and navigating a local pathway through a limited subset of “rhizomous connections”. This involves more than individual mental processes, because cognition is “distributed throughout physical, social, historical and institutional contexts” as well (Cunningham, 1998, p. 830). Models of knowledge work will be highly specific for different Discourses. As explained in the next section, sign systems are oriented for particular uses by specific Discourse communities (Morris, 1971) (as also explained by Gee, 1996) (refer to Section 3.5.1).

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4.3.4. Typology of Discourse in terms of signs systems

From a semiotic perspective, a Discourse is a structured, codified system of significations and its contents can be reduced to manageable formats, so that its messages may be studied according to its codes (Eco, 1990). Interpretations of messages however, also depend on the codes of addressees (p. 48). As Morris (1971) explains, “something is a sign only because it is interpreted as a sign of something by some interpreter” (p. 20). It is problematic however, to account for the habits, dispositions and experiences sign users bring to their interpretations of sign systems, because some interpretations are unacceptable. As Eco (1990) asserts, the rights of interpreters can be over stressed. The problem is not that texts have unique meanings guaranteed by some “interpretative authority”, but that meanings are constrained to the purposes of Discourses, and the Discourse community decides which interpretations are due (p. 21). This is particularly the case in scientific Discourses. There are many ways to categorise Discourses, for example referential and informative versus emotive and expressive (Morris, 1971, p. 173). Morris uses semiotic principles to classify Discourses on the basis of the modes of signifying and signs they use. “Designation”, Morris argues, is frequently taken as the baseline mode of signification, but it provides an inadequate account even for scientific Discourses, because there are always powerful non-designative influences as well (pp. 193-194). Nonetheless, scientific languages tends to be “designative in mode” and “informative in use”, and despite the fact that many modes of Discourse are present providing complementary viewpoints, science is concerned with “the search for reliable signs” and aspires to a “systematized body of true statements”, hypotheses if the evidence is minimal, and laws if the statements are confirmed (p. 206). Examples of other Discourses are technological (informative, prescriptive), political (valuative, prescriptive), and rhetorical (valuative, formative) (p. 205). Guiraud (1975) distinguishes between Discourses based on the nature of codes, for example, logical, scientific, cultural and aesthetic, which is helpful when pure disciplinary knowledge is being considered. In chemistry for example, a chemical structure denotes precisely a chemical compound adopted as convention by the community of chemists. In art, an aesthetic code such as a brush stroke may connote things for an interpreter quite divergent from the artists’ intentions, although the brushstroke becomes to a degree a convention denoting an art epoch or era. 139

Social codes inform the use of objects, for example cars primarily denote function, but cultural codes produce many connotations about status and wealth. Scientific codes tend to be “monosemic”, because the community of scientists tries to contain connotative variety or “polysemy”, using non-linguistic conventional codes such as mathematics and symbolic logic (Guiraud, 1975, pp. 25, 54-58). All modes of Discourse are polysemic, a point that has been forced to the surface in the so-called postmodern era. As Lyotard (1979/1984) explains, the denotative game of science in the modern era was the “game of truth” which sought to verify scientific statements and stabilise meanings. In the postmodern era, economic and social accountability are added to the game of science, which is also enhanced by the game of technology. In the production of transdisciplinary Mode 2 economically and socially accountable knowledge, the production of proof, the denotative game, falls under the sway of the game of technology, and an equation is set up between wealth (power), efficiency and truth (pp. 41-47) (refer to Section 3.2). All scientific Discourse defers to power and money, so that all scientific statements can be deconstructed to reveal ideological and manipulative strategies (Krips, McGuire, & Melia, 1995; Latour, 1987). There are three dimensions of semiotics that can be used to provide a structure for Discourses understood as Mode 2 knowledge systems, and for making the strategies used by knowledge workers and symbolic analysts more visible (Figure 4.13, adapted from Morris, 1971, p. 417).

syntactic dimension semantic dimension R 2 Designation O Denotation Referent R3

R1

R4 Interpretant I Interpretation Reference R = representation R5 O = object I = interpretant pragmatic dimension

Figure 4.13. The dimensions of semiotics (adapted from Morris, 1971, p.417).

The syntactic dimension exemplifies the relations between signs in their representative aspects; the semantic dimension exemplifies the relations between 140 signs and the objects of reference, or culturally coded meanings; the pragmatic dimension exemplifies the relations between signs and interpreters, who modify the rules to suit the circumstances. Rhetoric and ideology come to the surface in the way knowledge gets represented. Morris (1971) proposed that the syntactic, semantic and pragmatic dimensions of semiotics would unify the sciences (p. 307). Eco (1976) demonstrates how this can work in one comprehensive semiotic model as follows.

4.3.5 A syntactic, semantic and pragmatic model of Discourse

The semiotic model outlined in this section encompasses the structure and use of an object for analysis, described at the syntactic level for relations between its component elements, by relations of combination and association in the plane of expression; for the semantic relations each component element enters into with elements in the plane of content (expression-content and content-content relations), based on theory and rules of procedure (logic); and for pragmatic relations arising between signs and interpreters who modify the rules according to the circumstances of object use. Such analysis is conducted from a particular perspective, and ideology is revealed in the way things are interpreted and expressed (rhetoric). Because semantic fields are infinitely expandable, it is possible to work only with pertinent fragments. These fragments are structured sets of expression-content relations organised into networks or clusters of interconnected Discourse or cultural units (Eco, 1976, p. 62). Cultural units are selected from the continuum of a cultural phenomenon according to the interests of each Discourse community. In clinical chemistry for example, physical theory interconnects with mathematical and symbolic expressions in practice with the aid of technologies such as laboratory instruments. The knowledge worker negotiates a pathway through interconnected fragments of semantic fields guided by appropriate markers or cues, denotations and connotations, arising in specific contexts and circumstances. Because there are many pathways to choose for each cultural unit, some complementary and some contradictory, several modes of logic, sign action or semiosis are used. Deductive inference applies to the rule-governed use of laboratory instruments; abductive inference to troubleshooting errors, and inductive inference to the classification of instruments in order to make appropriate instrument selections in different analytical circumstances (see Sections 7.2 & 7.3). Each logical manoeuvre is based on 141 theoretical premises, but pragmatic circumstances in laboratories, such as staff, space and budgets, frequently override rules (see Section 8.3). Value orientations are represented in structured semantic fields and also in the rhetoric or framing of information, in graphs, charts and statistics, interpreted using multi-literacies (Lemke, 2000) (see Sections 7.3.3.2, 7.3.3.3, & 8.4). The terminology applied to semantic fields by Eco (1976), as explained in the next section, varies from that applied by Barthes, Hjelmslev and Saussure, to reflect the complicated range of activities implied in the three dimensions of semiotics.

4.3.5.1 The terminology applied to cultural units and semantic fields

Whereas signs are the province of general semiotics and philosophy, a specific semiotics is concerned less with signs than with the way they function in codes, axes and fields. Hjelmslev (1943/1961) applied the concept “sign function” to the expression-content relation as an entity, comprised of two “functives” (expression and content), serving as an “intermediate and combining concept in linguistics” by entering into mutual correlation with other sign functions (p. 33). The relations between functives are distinguished from the relations between ‘y’ and ‘x’ in an algebraic equation in which y is a function of x, because in the language sign function, “one functive has a function to the other”, in a sign entity which fulfils a definite role and a position in the chain of a text (p. 34). Eco (1976) says there is a sign-function whenever a cultural expression, a sign vehicle (SV), conveys some content to an addressee on the basis of an established social code or convention. The expression-content relationship is permitted because a code apportions the “elements of a conveying system” (plane of expression) to the “elements of a conveyed system” (plane of content) (p. 48). Note that Eco uses the term “sign vehicle” (SV) as used by Morris (1971) to indicate the representative aspect of the sign. SV roughly, but not strictly, equates with signifier (Saussure) and expression (Hjelmslev), and requires a support for the signification (Barthes), an immediate object and its representamen (Peirce). Eco uses the term “sememe” to replace the more static terms signified (Saussure) and content (Hjelmslev), to indicate a semantic unit that assumes the entire set of possible interpretants (Peirce) (Eco, 1976, p. 84). The sememe can be closely aligned with Hjelmslev’s external linguistic model (Figure 4.4). The sememe however, provides connotative markers to direct analysis towards specific purposes 142 so that semiotic drift is avoided. The graphic convention of guillemets is used to indicate a sememe <>; single slashes are used to indicate a verbal expression or sign-vehicle (SV) /xxxx/, the representative aspect of the sememe; and objects are indicated using double slashes //xxxx// (Eco, 1976, p. 31). For example, the word /car/ is a verbal expression of the object //car// representing a cultural unit with cultural content implied in the sememe <>. The cultural unit “car” is thus the unity of the SV /car/ or //car// and its content or sememe <>; so that /car/ or //car//=<>. For the most part this thesis is concerned with a semiotics of objects (especially laboratory instruments), so that the expression //xxxx// is applied to a specific object, otherwise the general cultural unit is given as SV=<>.

4.3.5.2 Cultural units structured in semantic fragments

Three dimensions of semiotics, syntactics, semantics and pragmatics are integrated in one model centred on the sememe SV=<> (Eco, 1976) (Figures 4.13 & 4.14). Pertinent fragments of a Discourse (form of the content) are selected from the substantive structure (substance of the content) for specific purposes, and interpretations are drawn around contextual markers or cues.

new tree….

Circ.1 d7/d8 Cont. A d2 SV

Cont. B d3/d4 SV SV…Sm = <> d1 Circ.2 d9/d10 Cont. C d5/d6 SV Ideol. c. (+) Circ.3 d11 SV new tree…. Ideol. c. (-)

SV = sign vehicle (object, expression) Sm = syntactic marker Syntactics Semantics, pragmatics d = denotation, c = connotation Plane of expression Plane of content Cont. = context, Circ. = circumstance Ideol.c. = ideological connotation

Figure 4.14. Semantic fragment.

Interpretive choices are indicated as conceptual oppositions and ideological bias is represented by positive and negative connotations. Ideological connotations 143 inevitably raise issues that are the concerns of other fields, so that the value orientations of Discourses are revealed. In the medical sciences for example, in the evaluation of laboratory tests in EBLM, even if results are validated at scientific and statistical levels, there are additional factors to consider, clinical relevance, economic viability and the social appropriateness of tests (e.g. the usefulness of a test for a genetic disease for which there is no cure). The interpretation of laboratory test information requires the symbolic analyst to interpret multiple representations of data and results in graphs, charts and statistics, and also awareness of other disciplinary perspectives (as discussed in Section 8.4). Semantic fragments are navigated in knowledge work for multiple purposes in three ways. Firstly, at the syntactical level, an object, expression or sign vehicle (SV) is analysed in the plane of expression (Section 4.2.2). The SV is represented as a Discourse unit and is displayed in a system of relations of combination (by division in the expression line) and paradigmatic associations (expression side) with other SV (Eco, 1976, pp. 72, 92). SV are classified according to differential features (invariants) in the axis of association with other SV (left side, Figure 4.14). Each association in the expression side constitutes another SV, entering into another set of relations of combination and difference with other SV. The points of significance (invariants or syntactic markers, Sm), direct the analysis towards pertinent semantic fragments in the plane of content. Secondly, meaning arises at the semantic level, following “componential analysis” of the sememe <> in the plane of content, <> being segmented into “elementary semic components” (denotations and connotations) based on contextual and circumstantial selections or “semantic markers” (Eco, 1976, pp. 72, 92). Cultural units become semantic entities when arranged with other semantic units in axes of oppositions and relationships providing a tree-like semantic structure (right side, Figure 4.14). There is however, no reliable way to distinguish between denotations and connotations, except to say that denotations are the “culturally recognized” connections that first correspond to the SV (e.g. a fur coat is worn primarily to keep out the cold). Many connotations or secondary meanings arise from primary denotations, and they are not necessarily culturally recognized (p. 86). A denotative or connotative marker (circumstantial or contextual cue) in the semantic fragment in turn directs the analysis towards the pertinent syntactic marker (Sm) and SV in the plane of expression, thus providing the starting point for a new “compositional tree” 144

(p. 92). Note that Sm in relation to SV might be equivalent to the “ground” of the representamen referred to by Peirce (1931-58, 2.228), and the point of invariance in the signifying matrix identified by Barthes (1964/1973), each being points in the object of interest from which the signification emerges (Section 4.2.3.2). The sign triad (Figure 4.10a) specifies logic, in terms of the immediate object (O) that sets semiosis or sign action in motion (e.g. symptom or clue), by requiring a deliberate act of inference in order for interpretation (I) to take place. Thirdly, in the pragmatic dimension, each denotation or connotation comprising the semantic fragment becomes a marker for another SV, each open to its own componential analysis and interpretation represented by a new system of SV. A complex grid or “rhizomous” network of connections in a global semantic system would emerge if all possible denotations and connotations of cultural units (SV=<>) were considered, as Figure 4.14 barely begins to suggest. The semantic fragment is thus derived from an “n-dimensional” model of “infinite semantic recursivity” that circumscribes the “polydimensional universe of meanings” (attributed to M. Ross Quillian, the semantic model used in early attempts to construct AI [Eco, 1976, pp. 121-123; Jackson, 1999, p. 104]). A knowledge worker will navigate a pathway through this rhizomous network applying inferential reasoning for a specific purpose, and the chain of interpretants or unlimited semiosis, ceases once the final interpretant or goal of the activity is reached. Several forms of reasoning apply in the process, which explains what knowledge workers and symbolic analysts do in the laboratory. Firstly, logical connections are made between the pre-interpretive object of a sign and its interpretation, rule-governed (theoretical), deductive in ideal situations, inductive for classificatory activities, and abductive for detection and diagnosis of errors (as demonstrated in Sections 7.2 & 7.3). Secondly, pragmatic decisions override theoretical considerations in certain circumstances. Thirdly, whereas restrictions are placed on multiple readings by contextual and circumstantial markers in order to make a reading useful, there are choices of readings, some complementary and some contradictory, and value orientations are brought to the surface in these contradictory choices (Figure 4.14). By making ideological choices explicit, semiotics becomes a vehicle of social criticism (Eco, 1976, p. 289). Finally, as a function of ideology criticism, the symbolic analyst can discern the rhetorical or persuasive effects used in communicating information, by its representation in different forms of expression. 145

As Eco (1976) explains, an artist will deliberately plan ambiguity of textual readings by manipulating art forms for aesthetic effects, the purpose of which is to incite audiences to deliberate acts of interpretation (p. 261). A function of art is thus to elicit a response in order to break the “anaesthetic” hold on audiences otherwise habituated to the cultural codes or structures on which different social formations rest. Problems arise, however, when a message sender surreptitiously suppresses certain semantic readings, and highlights those that favour vested interests. In such cases the semantic space is manipulated in a deliberate attempt to persuade others which interpretations to make. Rhetoric, despite its adverse press, Eco (1976) explains, is not necessarily a “fraudulent procedure”, but a “technique of ‘reasonable’ human interaction controlled by doubt” and subject to pragmatic conditions (p. 278). Because human reasoning about facts, issues and statements is based on opinions, beliefs, values and experiences, everything rests on decisions, and it is the “aberrant performance” of rhetoric, ideology, which must be guarded against (p. 278). Ideology has many meanings, and applied to Discourses, ideological bias will be demonstrated by a commitment to a belief system, its language, knowledge, practices and values; which in some cases excludes others in the interests of power and maintaining control (Gee, 1996; O’Sullivan et al., 1994). A crucial aspect of “D” competence, knowledge work and symbolic analysis, is therefore the ability of an addressee to “disambiguate” messages that are confounded by multiple possible interpretations, and the ability to discern ideological biases that “load the sememe” with positive or negative connotations (Eco, 1976, pp. 139-142). Knowledge workers are interpreters who can “abduce” the presuppositions of message senders, the nature of their worldviews, and pragmatic motivations and emotional factors, and social, political, and personal factors that go into the constructions of messages (p. 277). The statement /He follows Marx/ for example, requires “disambiguation” in order to place it in the right context for interpretation (Eco, 1976, p. 289) (Figure 4.15). The contextual markers “movies” and “politics” denote Groucho and Karl respectively. If “Karl” is considered in the context of politics, the statement /He follow Marx/ denotes <>. This statement however, carries further positive and negative ideological connotations depending on the reader’s point of view. For example, in the circumstance of Prague post World War 2, the reading could carry a negative connotation <>. In the context of 146 universities and student politics in the 1960s, the reading could carry a positive connotation <>, according to fashion, or a negative connotation by those who are politically conservative.

Cont. history d. postdates Karl /He follows Marx/ =<> Cont. Politics d. disciple of Karl

Circ.1 Prague post WW2 Ideol. c. (-) Cont. Movies d. Groucho communist dangerous adversary Circ.2 Student movement 1960s Ideol. c. (+) Intellectual socialist idealist

Figure 4.15. Ideological connotations.

The representation of ideological choices in this manner makes a “semiotics of the code” an “operational device in the service of a semiotics of sign production” (Eco, 1976, p. 128), and demonstrates how the semiotic code can be used to represent knowledge of the encyclopaedia (Eco, 1984, 1990). Although the code defines sign functions based on coded cultural conventions, there are many pragmatic considerations that influence the way sign functions are viewed. The application of semantic fields in this way is therefore based on the presupposition that they are useful, but temporary devices for explaining messages, the “regulative hypotheses” needed to control the immediate semantic environment of given cultural units (Eco, 1976, p. 128). They do not provide definitive structures because once such a system is described it is already changed due to historical factors and the critical erosion that results from submitting phenomena to analysis (akin to the indeterminacy principle in physics) (p. 129). A brief illustration of how semiotic analysis might be applied to the transportation system follows, building on Eco’s example (1976, p. 27).

4.3.5.3 Transportation as cultural phenomenon

Transportation is a phenomenon relevant to all cultures no matter how technologically advanced or primitive. All cultures share the primary denotation about transport, as the means by which people move about, and an infinite variety of 147 connotations apply to transport once cultural perspectives, contexts, social, political and economic circumstances are considered. The analysis will begin by ordering the phenomenon transport to particular disciplinary perspectives (substance of the content), and technological applications (substance of the expression), under the cultural sign model (Figure 4.16). Transportation can be ordered by fields such as history, politics, economics, and anthropology, and will be expressed in different technologies. Historians and cultural anthropologists will document the cultural transformations coded in forms of transport; scientists and inventors will monitor their technological development; government bureaucracies will monitor their civic utility and economic viability; and social scientists their social utility and symbolic values.

Transportation

History, Politics, Economics, Sociology, Anthropology Formations in transport C S F Mechanical, social, bureaucratic

E F Morphology of transport, parts, types S Technology, material substance, wood, metal, plastics etc.

Purport/matter/continuum

Figure 4.16. Cultural sign model of transportation.

In order to say something purposeful about transport, it is necessary to place restrictions on the infinite pathways opened up by the cultural sign model. The analysis will begin with a semantic fragment built up from a particular cultural unit of transport (SV=<>), selected as pertinent to the Discourse initiating the analysis. Because semantic relations can be infinitely expanded, the analysis requires restraint by several moves. Beginning with a single transport unit, //car// becomes a semantic entity once it enters into coded relations with the cultural content <> (Figure 4.17). The object of signification //car// can be analysed in the plane of expression, in the expression line for the relations of combination of its component elements, thus providing descriptions of its morphological characteristics. In the expression side comparisons are made by associations with other forms of transport, 148 arising at significant points of invariance (syntactic markers, Sm). The analysis thus moves from analytic description of one mode of transport to its comparison and classification with other modes of transport (alternatives, bike, train, bus etc.). Each transport SV will in turn enter into coded relations with specific semantic contents <>, and once the semantic relations are considered, a large network of possible connections is generated. It is necessary to be selective and consider the context and circumstances of the analysis, denotative and connotative semantic markers that elicit different interpretations about transport. Knowledge about transport arising from its classificatory schema in the plane of expression will be considered at a number of levels in the plane of content, for example historical, mechanical, political, and social, thus producing a multi-levelled analysis of transport. The analysis must be goal oriented if semiotic drift is to be avoided.

Plane of expression Plane of content

Expression side d1. mode of transport Cont. Mechanics c. Technological progress //carriage//

//train// //car//...Sm =<> Cont. History Circ. Museum d. horse drawn Expression line d. engine //bus// d. Steam Cont. Politics d. Gasoline //taxi// d. Electric

//bike// Cont. Social c. wealth & status Circ. Prestige cars //feet// c (+) successful c (-) criminal activity Cont. Health Circ. Physical fitness c (+) Health consciousness Cont. = context c (-) Fanaticism Circ. = circumstance d = denotation Cont. Environment c = connotation c (+) social responsibility Sm = syntactic marker (Invariant) c(+) c (-) ideological connotations

Figure 4.17. Semantic fragment of transportation.

Consider //carriage// for example in the plane of expression. In the context of history and circumstance museum in the plane of content, the conceptual opposition engine-driven/horse-drawn is denoted. Engine in turn evokes the conceptual oppositions, steam-driven/gasoline, and gasoline/electric, which are in turn connotative markers of technological progress. In the context of economics, the horse driven/engine driven opposition might be a marker of economic and social status, evoking the conceptual opposition rich/poor. These oppositions are in turn markers 149 for alternative modes of transport in the plane of expression, //taxi//, //bus//, //bike//, and //feet//, for example, but also introduce an evaluative component. Whereas //bike// denotes a two-wheeled mode of transport with practical utility for getting around cheaply, it also carries connotations depending on the social and economic circumstances of would be bike-users. The SV //bike// might carry the connotation not wealthy enough to afford a car. In the context of the environment it might carry the positive connotation, enlightened and socially responsible. In the context of health, //bike// might carry the positive connotation, health consciousness, but also the negative connotation, competitiveness, body fetishism and fitness fanaticism. The process of adding connotation to connotation continues indefinitely and must be restricted by the criteria of pertinence. Even at the restricted level there are contradictory choices in readings, so that the analyst must decide which readings to make. Interpretive choices are motivated by values, beliefs, emotions and opinions of the analyst, as much as by disciplinary knowledge about transport. If the analysis is subject matter for local government transport policy debate, the analyst might try to persuade audiences (e.g. residents living in areas targeted for motorways, busways, and bridges), which are the right interpretations to make. The art of persuasion in technical Discourses such as transportation can be demonstrated in the use of graphs, charts and statistics applied to transport usage (see Lemke, 1995; Latour, 1987, Muir- Gray, 1997). A complex cultural phenomenon such as transportation cannot be understood from a single perspective (Eco, 1976, p. 27). Morris (1971) also explains that it is important to distinguish between primary and secondary uses of signs and objects, and the social consequences of their existence (p. 174). The full story of steam engines for example, goes much further than describing their material and mechanical characteristics and functions. Understanding about engines is multiplied when their history and the social conditions of invention and use are considered, including bureaucratic and banking institutions that provide the finance, and effects on neighbouring countries (p. 174). Sociological approaches to semiotics are particularly concerned with the way social groups assign meaning to objects in their material aspects (Eco, 1976, pp. 27-28) (see also Gottdiener, 1995, p. 178). Objects participate in social arrangements, and in the process acquire “use values”, “exchange values” as commodities in complex economic systems, and “sign values” which signify social status (Baudrillard, 1981, 1968/1996). When an object acquires 150 sign values in a social context, its second order meanings are not immediately apparent, but can be detected by the cultural analyst. Social values accord many different meanings to objects, and the same object can evoke contradictory interpretations. A prestige car for example, might carry positive connotations of wealth and success for some people; and for others carry negative connotations, implying underhand criminal activities such as drug dealing associated with large amount of money (Figure 4.17). The analysis of transportation ends here because there is no specific question about transport to answer in this case. It is emphasised that this model can be used to give structure to connotative analysis, otherwise reliant on an analysts’ creativity (e.g. Baudrillard, 1968/196) (Section 4.2.4.2). Alternatively, Foucault’s archaeological approach to analysis might be used (1969/1972).

4.4 Conclusion

This chapter distils some key principles from semiotic theory and levels of analysis that can be applied to the structure of contemporary clinical chemistry knowledge and knowledge work and symbolic analysis in laboratory practice. Figure 4.18 expands on Figure 4.7 by providing more details of the levels addressed in semiotic analysis of scientific Discourse (explained further below).

Unstructured analysis Unscientific Connotation

IV E C Operation

E C III Rhetoric & Ideology Statement Observation E C II System Hypothesis experiment Object I E C

Plane of expression Structured analysis Scientific a • b • c • E line Plane of content Denotation ≠≠ ≠ Theory net Division Architectonic a b c 1 1 1 Invariant ≠≠ ≠ Classification a2 b2 c3 E side

Figure 4.18. Levels of analysis of scientific Discourse. 151

The visibility of knowledge work can be improved by mapping local semantic fragments of knowledge, and demonstrating the logical and discursive modes of reasoning knowledge workers use by integrating fragments of knowledge in practice situations: rule-governed procedures, instrument classification and use; troubleshooting errors; pragmatic decision-making in circumstances overriding theory; and the art of persuasion in communicating information. The capacity is also demonstrated for critical reflection on the values in which Discourses are based; and multi-literacies are used for manipulating and interpreting the multiple forms in which scientific information is represented. In semiotic analysis, a cultural, or Discourse unit can be organised under the cultural sign model (Figure 4.4), and the four levels of analysis defined by semiology apply also to scientific Discourse (Figure 4.18). Scientific, structured, aspects of knowledge are considered in terms of division, classification, and system, and logic applies to the verification and applications of scientific statements (below solid line). Less structured, unscientific, connotative analysis is applied in order to gain insights into cultural experience and practice (above solid line). There is much common ground between semiology (Section 4.2) and semiotics (Section 4.3), but in semiotics connotation is given structure (Figure 4.14), and in semiology, logic is not considered. This chapter demonstrates that semiotics has wide applicability in many fields, and its applicability to clinical chemistry knowledge structure and laboratory practice will be demonstrated in subsequent chapters following the design of semiotic analyses in Chapter 5.

152

Chapter 5 Design methodology for semiotic analysis

5.1 Introduction

Analyses are designed in this chapter to demonstrate the extent to which semiotic theory can facilitate understanding of contemporary clinical chemistry knowledge and practice. This will be achieved by a selective analysis of clinical chemistry data sources gathered from the Australasian Association of Clinical Biochemists (AACB), pathology industry and an undergraduate teaching situation. The analyses are made “operational” by linking qualitative data sources, documents, observations, artefacts, and archival records, and the research questions in the different aspects of semiotic analysis (as summarised in Figure 4.18) (Krathwohl, 1998; Miles, & Huberman, 1994; Yin, 1994). The epistemological assumptions underpinning the design of semiotic analysis are addressed in this section; and semiotic analysis is located in the cultural studies paradigm to guide data collection techniques and ethical issues in Section 5.2. Its application to clinical chemistry is described in Section 5.3. In designing these analyses, a distinction is drawn between theory-driven, deductive qualitative research, in which theory directs data collection and data analysis, and the reverse situation in which data collection and analysis techniques drive the research process in order to induce grounded theories of culture (Coffey, & Atkinson, 1996; Krathwohl, 1998; Wolcott, 1992; Yin, 1994). Whereas the latter inductive approach demonstrates a methodological commitment to qualitative work, the former deductive approach demonstrates a commitment to pre-given theory, but using qualitative data collection and analysis techniques (Coffey, & Atkinson, 1996, p. 142). In both cases, theory testing and theory building, clear links are made between theory, evidence and conclusions in data analysis, but the data sources in theory-driven research are selected purposively to demonstrate the logic of the theory being tested (Krathwohl, 1998; Miles, & Huberman, 1994; Yin, 1994). Theory applies in certain conditions and based on certain assumptions and presuppositions. In the semiotic analysis of culture, assumptions are made about the way cultures are structured and function, and it is presupposed that individuals share ways of 153 speaking, acting, valuing and believing, and of using languages, signs, tools and models in culturally specific activities (refer to Section 4.3) (see also Bunn, 1981; Eco, 1976; Morris, 1971). Diversity in cultures is also acknowledged, and although participants are constrained by contextual and institutional factors, as active participants and through sub-cultural resistance, they are also agents of cultural change (Bennett, 1998; Gottdiener, 1995). Data analysis in this chapter explains what and how different sources of evidence are used to demonstrate the effectiveness of semiotics in clinical chemistry, to address the four research questions posed in Section 1.4: What structure of clinical chemistry knowledge applies in automated computerised laboratories in the era of socially accountable laboratory medicine? What contextual factors constrain knowledge work in automated computerised laboratories? What modes of reasoning do knowledge workers apply, and what adds value to clinical chemistry laboratory practice? What range of competencies do knowledge workers need for contemporary clinical chemistry practices, and what additional skills will add value in socially accountable laboratory medicine? This chapter describes the data sources and data analysis strategies used to demonstrate that semiotics provides a powerful tool for answering these questions. In the next section, semiotic analysis is located within a diverse set of traditions and approaches to cultural interpretation, in order to identify data sources, data collection techniques, and reliability, validity and ethical issues arising in cultural analysis.

5.2 Research strategies used in cultural analysis

Semiotic theory is based on assumptions about the way cultures are structured and function (Section 4.3), but applied to pragmatic qualitative research questions is subject to the same kinds of constraints, ethical issues and logistical factors applied to any other form of cultural analysis. The semiotic framework applied to clinical chemistry in this thesis (as summarised in Figure 4.18) is derived from semiologists and semioticians whose main interests are in language and literary theory (Barthes, 1964/1973, 1967/1990; Eco, 1976; Hjelmslev, 1943/1961; Saussure, 1959), as well as science philosophy (Morris, 1971; Peirce, 1931-58). This section examines more recent approaches to cultural analysis to find out what kinds of resources are used in 154 cultural analysis in educational settings, and to identify the pitfalls, and reliability and validity issues pertaining to cultural analysis.

5.2.1 Paradigms, traditions and approaches

Perspectives on culture are narrowed in this section to those that account for the use of signs, representations and tools in cultural contexts, in keeping with the semiotic and socio-cultural perspectives of laboratory life sought in this thesis. Approaches to cultural analysis are considered using the terms paradigms, traditions and approaches interchangeably under the one cultural studies umbrella, because cultural studies supports many different perspectives of culture (Bennett, 1998; Frow, & Morris, 2000). Ideas about culture and approaches to cultural analysis have changed over the last century. In traditional cultural anthropology, isolated fieldworkers immersed themselves in the everyday lives of “exotic” human cultures, and conducted ethnographic research using first hand knowledge of social processes gathered in situ (Hammersley, 1984, p. 5) (see also Atkinson, & Hammersley, 1994; Creswell, 1998; Tedlock, 2000; Wolcott, 1992). The cultural interpretations arising from this form of research were drawn from extensive field-notes, participant observations, interviews, documents and cultural artefacts, from which patterns of behaviour, rituals, customs, and ways of life of communities emerged (Creswell, 1998, p. 58). This process describes the “first moment” of qualitative research and is exemplified in the studies of famous anthropologists such as Malinowski, Margaret Mead, and Gregory Bateson (Denzin, & Lincoln, 1994, p. 7). Many errors in the qualitative research process have come to light since the early stages of cultural analysis. Examples of such errors are unacknowledged researcher bias; unethical treatment of research subjects (e.g. deception about the purpose of the research); manipulation of the research situation by participants telling researchers what they thought they wanted to hear; and researchers being coopted to the native situation (going native), thereby losing all sense of the research perspective (Creswell, 1998, p. 60). Informed consent from research participants became an issue, and also data confidentiality and ownership of data (Krathwohl, 1998, p. 208-217). As a result of the “crisis” in cultural anthropology emerging from the identification of research errors, and also political upheavals in the 1960s and 1970s, more self-reflexive approaches to cultural interpretation emerged and also 155 different data sources were sought than those that were directly aimed at so-called “lived experience”, such as interview and observation data (Denzin, & Lincoln, 2000, pp. 16-17). Geertz (1973) is credited with bringing to wide attention the idea that all anthropological writings are “interpretations of interpretations” and that the observer/researcher has “no privileged voice” (Denzin, & Lincoln, 2000, p. 16). In the so-called “blurred genres” period (1970-1986), “thick descriptions” of cultural events, rituals and customs, were provided, by employing a wide range of paradigms, strategies and methods. There is such a wide variety of methods and cross-cultural perspectives now in use that cultural anthropology has lost its central position (Bennett, 1998; Readings, 1996; Shore, 1996). Cultural interpretations are viewed as simply versions of “reality” offered by a researcher drawing on his/her experience as a resource. The constraints of the research situation are acknowledged, and also researcher relationships with the subjects of the research, who are in some cases given a “voice” (Lather, 1991; Lemke, 1998b; Marshall, & Rossman, 1999; Van Maanen, 1995). The importance of cultural analysis lies not with claims made about cultures, but with the contributions made to understanding of social issues (Creswell, 1998, p. 195). Cultural interpretations are expected to be ambiguous, situated, local, particular, partial, and to leave gaps that raise additional questions (p. 198). There are now many approaches to cultural analysis but they have one thing in common. In addressing cultural misrepresentation and unethical treatment of research subjects, they turn the research process on objects close to home, including the research process itself (Carpsecken, & Apple, 1992; Frow, & Morris, 2000; Kemmis, & McTaggart, 2000; Quantz, 1992). “Ordinary” culture is examined in local situations, schools, classrooms, hospitals and communities, and the potential for treating sub- cultures as exotic is addressed by ethical treatment of the research subjects, and by the researcher reflexively examining his/her motivations and biases in the research (Frow, & Morris, 2000, pp. 327-329). Cultural studies emerged as a field of inquiry, some say a paradigm, to accommodate many different views about culture and in an effort to resolve doctrinal disputes between conflicting knowledge systems (Frow, & Morris, 2000, p. 332; Readings, 1996, p. 101). In cultural studies perspectives, culture is “plastic”, being moulded by both powerful elites and resistant sub-cultures (Frow, & Morris, 2000, p. 315). In cultural studies, there is flexibility about methods of data collection and data 156 analysis, so that formal systems of textual analysis including semiotics are considered in addition to the power relations, places and histories which frame the cultural experience (pp. 326-329). Power relations are given considerable attention because of the way they influence cultural production and its circulation, and make individuals “different” because of their gender, class, race, or religion. Sub-cultural resistance is addressed because individuals exert reciprocal and revisionary cultural effects (Bennett, 1998, pp. 171-173). The whole way of life perspective of culture (Williams, 1963) dating from the mid-twentieth century provided an alternative to earlier elitist perspectives of culture (e.g. high culture in the Oxford/Cambridge literary tradition [Docker, 1994]). The egalitarian, mass culture perspective encompasses all the mundane aspects of culture such as shopping and recreational pursuits, which ushered in wide application of cultural analysis to “everyday cultural practices”. The cultural practices of “young people” were of particular interest because they brought to light the relations between dominant, resistant, and emergent forms of culture (Bennett, 1998, p. 23) (see also Hall, & Jefferson, 1975/1993). In the new perspectives, culture became the “maps of meaning” which make things intelligible to members of social groups. These maps encompassed “the meanings, values, and ideas embodied in institutions, in social relations, in systems of beliefs, in mores and customs, in the use of objects and material life” (Clarke, Hall, Jefferson, & Roberts, 1975/1993, pp 10-11). Geertz (1973) adopted a symbolic approach to cultural interpretation based on the assumption that humans are animals suspended in cultural “webs of significance” they weave for themselves (p. 5). Culture is thus whatever an individual needs to know or believe in order to operate in a manner acceptable to the group (p. 11). Cultural participants need to be in command of “socially established structures of meaning”, codified into plans, recipes, rules, and procedures that govern behaviour (pp. 12, 44). There are also other factors that underlie human conditions, including selective evolutionary factors, and biological and physical necessities that are interwoven with political and economic realities (p. 30). Culture is thus defined in terms similar to the internal and external structure of language, as it is structured in signs (Section 4.2.2); and in terms similar to socio-cultural perspectives of literacy and discourses, as specific ways of seeing and operating in the world using specialised languages and tools (Section 3.5.1). 157

Semiotics is a suitable substitute for symbolic anthropology and discourse theory because it provides a tool for analysis of signification and communication in culture, based on the assumption that “the laws of signification are the laws of culture” (Eco, 1976, p. 28). Cultures, as systems of signification are codified in sign structures for which rules, procedures and logic applies. Cultures are also systems of communication and are modified by participants for creative, pragmatic and ideological purposes (p. 27) (see Section 4.3.5). An additional source of crossover between semiotics and cultural anthropology arises with neo-Vygotskian perspectives (following Vygotsky, 1978) which are used widely in educational research to examine the relations between “culture, context and cognition” (Jacob, 1992). The assumption is made that cognitive processes are mediated by “technical tools” such as pens, papers and computers, and the “psychological tools” of signs and representations such as verbal languages, mathematics and pictures. Cognitive processes are also placed in the contexts of history and social institutions and the social interactions that take place within them (pp. 307-310). Neo-Vygotskian research is conducted in everyday contexts such as workplaces and shops, using participant observations, interviews and diaries as data in order to understand how people learn in the context of specific cultural activities (Jacob, 1992, p. 312) (e.g. Lave, 1988; Lave, & Wenger, 1992; Scribner, & Cole, 1982). The physical characteristics of contexts are considered for the constraints they impose on individuals, for how they reflect a larger social order; and for the way cultural settings are redefined as people use tools in specific cultural environments (Jacob, 1992, p. 316). Representations, environments, artefacts, tools, history, social interactions, and behaviours, all constitute suitable data for cultural and semiotic analysis. A focus is sought for semiotic analysis applied to clinical chemistry in the next section.

5.2.2 Data collection techniques for semiotic analysis

Given the aim of this thesis to define knowledge work and symbolic analysis in laboratory practice using a semiotic framework, the focus of data collection is turned towards tool use (laboratory instruments), contexts (laboratories), papers and inscriptions (graphs, charts and statistics). Data are needed that provide evidence of the codes of laboratory practice (e.g. documents, textbooks, and course materials); 158 that demonstrate the way codes are used (e.g. observations of instruments, and their use); and evidence of laboratory experience as it is inscribed in the material culture of the laboratory, in objects and spatial arrangements, as captured in diagrams and photographs. Formal and social semiotic approaches are needed to encompass the structure of knowledge, the logic of practice, and laboratory experience, and representations are objects for analysis in all semiotic approaches. In data selection and data collection a distinction is drawn, as noted in Section 5.1, between constructing theory and testing theory, because the logic of data collection and data analysis is different in each case (Coffey, & Atkinson, 1997; Krathwohl, 1998). In order to test semiotic theory, non-probability samples are selected from different data sets, deliberately or purposively, to demonstrate deductively, how semiotic theory works (Krathwohl, 1998, p. 172). In order to construct theory, a much wider sample is needed, so that patterns can be induced by detailed data reduction and analysis, as occurs in ethnographic fieldwork (p. 249). In some cases fieldwork is used to create a representation of culture that captures the “lived experiences” of the people within, in terms of power relations, using observation and interview techniques (Quantz, 1992, p. 448). Other approaches avoid these subjective forms of data by seeking out cultural experiences as they are embedded in history (Cohen, Manion, & Morrison, 2000; Foucault 1969/1972; Leedy, 1997) and “material culture” (Gottdiener, 1995; Hodder, 1994; Tilley, 1990). Art, objects, tools, documents, papers and other physical artefacts constitute “mute material evidence” providing intended and unintended residues of human activities (Hodder, 1994, p. 395). Cultural traces are found in buildings, objects, documents and papers, from which historical researchers draw insights into past social and cultural interactions, in order to understand present social situations, and to anticipate future trends (Cohen et al., 2000, p. 158). In the analysis of material culture, the “flow of power” is sought (following Foucault, 1969/1972) through discourses and their manifestation in the material world. Of particular interest are the cultural processes and vested interests inscribed in mass cultural products created by “dominant” groups (Barthes’ logotechniques as explained in Section 4.2.3.3). Material culture analysis also seeks the sub-cultural resistance to dominant social structures inscribed in cultural products (Bennett, 1998; Gottdiener, 1995). These approaches to material culture are important given that sign systems and semiotic logic are relatively straightforward when denotations in science culture are 159 considered, but become complicated when connotations are built upon denoted systems in the socio-semiotic analysis of laboratory life and experience. The socio-semiotic approach to cultural experience explores the ideology inscribed in material forms and spatial arrangements, by integrating semiotics and sociology, and also drawing insights from discourse theory (Gottdiener, 1995; Foucault, 1969/1972). In socio-semiotic approaches, the analyses are organised around the cultural sign model (expression and content, forms and substances) derived from Hjelmslev’s structural linguistics (Figure 4.4). Contexts, spaces, rooms and buildings are thus prime data sources in socio-semiotic analysis. Many layers are considered in cultural analysis, social practices, institutions, forms of agency, class, race, and gender, economics, aesthetics, philosophy, history, geography, and sociology (Frow, & Morris, 2000). In the socio-semiotic framework, these layers constitute the degrees of language by which the internal language system (grammar) is connected with the external world through connotations (Sections 4.2.2, 4.2.3.3 & 4.2.4.2). Whole ways of life can be inscribed in material culture in this manner, so that contexts such as classrooms, shops, hospitals, prisons, shopping malls, theme parks, leisure centres, housing estates, workplaces, schools, and classrooms, are analysed as forms of spatial practice (Gottdiener, 1995) (Section 4.2.4). Sociological insights can be drawn from the laboratory when its spatial arrangements and tools or instruments of analysis are connected by connotation to the social system medical laboratory science represents (Shapiro, 1998). In addition to spaces and objects, there are representations as data sources to consider. In cultural studies there is a particular interest in communications media, film and television, for the way they shape audiences which in turn challenge and transform the meanings circulated by these “culture industries” (Frow, & Morris, 2000, pp. 321-323). Educational media however, are examined for specific “framing effects” that are believed to exert direct learning effects (Kress, & van Leeuwen, 1996; Salomon, 1994). The interactions between mind, media and representations are sought in the “frame compositions” of educational media, and their symbolic features, “iconicity” and “resemblance” for example, for the different mental skills they cultivate towards knowledge acquisition (Salomon, 1994, p. 29). The “visual grammar” of educational media design is examined in some cases for compositional components such as foregrounding and vectors, in order to seek insights into visual literacy (Kress, & van Leeuwen, 1996). More specifically, the multimedia demands 160 of science curricula are examined, and the different modalities in which scientific information is represented, in mixed verbal language, mathematical equations, diagrams, charts, and tables (Lemke, 1998a, 2000). “Multi-media semiotics” constitute the “complex representational apparatus of scientific reasoning, calculation, and practice”, and are each suitable data sources for semiotic analysis (Lemke, 2000, p. 247). Graphs, charts, statistics and other forms of scientific representation are paramount sources of data when semiotic analysis of laboratory practice is considered. This is because logic can be demonstrated in the interactions between representations, objects and interpretations (Sections 4.3.2 & 4.3.3). Much more than logic can be examined in the analysis of scientific representations. As Latour (1987, 1990) explains, laboratory inscriptions play a fundamental role in scientific communications, because they are assembled in presentations for persuasive effects in order to attract the finance needed to fund scientific experiments. The “framing effect” in this persuasive, rhetorical sense has attracted the attention of Evidence-Based Medicine (EBM). Visual literacy in this sense is needed because it guards against misrepresentation of medical information (Muir- Gray, 1997). In the semiotic analysis of educational situations significance is sought in many different things, in objects, places, buildings, pictures, diagrams, graphs, equations, symbols, verbal language, behaviours, social interactions, and in documentation of disciplinary knowledge. Semiotic analysis then looks at what happens to culturally coded significances once they are subjected to interpretations in different contexts and circumstances, and from different ideological positions. Semiotic analysis also seeks out the constraints of contexts, such as institutional power structures, discourses and technological or material resources. How semiotic analysis can be applied in clinical chemistry is explored in the next section.

5.3 Cultural analysis applied to clinical chemistry

Several aspects of semiotic analysis are applicable to clinical chemistry. It can be used to give structure to the knowledge base; for description of the laboratory context in which the study is based, and for its comparison with pathology industry laboratories in order to gain insights into work and experience; for fragmentation of the knowledge base in order to demonstrate how knowledge workers apply logic in 161 laboratory practice; and for demonstrating that multi-literacies are used by symbolic analysts in their manipulations and interpretations of laboratory data and results in graphs, charts and statistics. This section describes the laboratory context, identifies the research participants, ethical issues, data sources and data collection techniques, and the way they will be matched in semiotic analyses in response to the research questions.

5.3.1 Research location and participants

The process of reengineering of pathology industry laboratories and medical scientists’ work roles within them was discussed in Chapter 2, but more specific treatment of the clinical chemistry laboratory is required in order to gain insights into laboratory work experience. This research is located within a clinical chemistry laboratory, clinical chemistry being a core unit in a medical science course offered at an Australian university of technology. In order to gain insights into contemporary laboratory practices it is necessary to consider industry laboratories as well. Before describing the wide range of data sources gathered from teaching and industry laboratory settings, this section introduces the primary laboratory site, research participants, and gatekeepers, and addresses ethical issues and problems arising in the conduct of this research. The teaching of clinical chemistry laboratory classes takes place in two laboratories, shared with other biochemistry and molecular biology classes, one designed for students’ performance of experiments, and the other for measurement of the products of experiments on analytical instruments, hereafter referred to as the instrument laboratory. Access to these laboratory sites would ordinarily constitute a major barrier to qualitative research (Creswell, 1998; Krathwohl, 1998), but was possible in this case because the researcher is a demonstrator in laboratory classes, and the “gatekeepers”, the academic supervisor of the biochemistry section, and the academic in charge of clinical chemistry classes, gave their permission. Clinical biochemistry course materials have also been provided (see Appendices B. & C.) (These gatekeepers are acknowledged at the front of this thesis). The instrument laboratory, as it was at the time of its description along with the collection of other data sources including instrument printouts and practical reports in 1998, provides the main focus of attention, because it approximates specialised sections of industry 162 laboratories. Access to industry laboratories was limited to site visits for the purposes of professional AACB activities. Teaching and industry laboratories are described in more detail in Sections 5.3.2, 5.3.3, 6.3.1 and 6.3.2. It is commonly understood that qualitative research, especially ethnographic fieldwork, is difficult to conduct on home territory (Creswell, 1998; Krathwohl, 1998, Wolcott, 1992). As Creswell (1998) explains, there may be conflicts of interest between work and research, and many ethical and logistical problems must be addressed (p. 114). The difficulties arising in the conduct of this research were associated in particular with the attempts to acquire participant-observation data of students’ laboratory performances (as explained in Section 5.3.2). The teaching demands were too great to permit detailed observations and recordings, and as a result no detailed field-notes of performance were obtained. However, given that the research is theory-driven, semiotics applied to clinical chemistry knowledge which is in turn based on theory, only a few non-probability data samples are needed, selected purposively, to demonstrate the logic of semiotic theory (Krathwohl, 1998, p. 172). There are nonetheless students and other laboratory demonstrators to consider because they have become inadvertent participants. This is because students’ practical reports were collected as archival records of their ability to handle data in graphs, charts and statistics, the analysis of which was conducted on the basis of demonstrators’ comments marked in red pen (as explained in Section 5.3.2). This practice could be construed as a form of “covert observation” because the students and demonstrators have not been informed of the process (Krathwohl, 1998, p. 250). However no deception of subjects was intended because the analysis of reports was conducted long after students and demonstrators had departed, and their anonymity has been ensured by the removal of their names (pp. 214-215). This source of data met with the approval of the university ethics committees, and the practical reports remain the property of the academic in charge of clinical chemistry. The purpose of laboratory classes is to teach students to conduct diagnostic tests such as blood glucose, manually and using automatic and semi-automatic instruments, individually, in pairs or in groups depending on the availability of resources and equipment. In the course of these experiments, students obtain reaction mixtures of standards and tests, which they present to measuring instruments and record the response which is given in numbers. Students are then required to manipulate these data into tables, graphs and charts, and to calculate a final result for 163 each simulated patient (e.g. Appendix B.). The data collection process explained in the next section is directed towards the disciplinary knowledge needed for these activities; the laboratory context for its instruments and spatial arrangements; and the logic of laboratory practice, as captured in laboratory inscriptions.

5.3.2 Data sources and data collection techniques

It is convenient to organise data sources into categories, even though there is cross over between them, for example observation data; interview data (also surveys and questionnaires); and review, archival or historical data (e.g. documents and artefacts) (Erickson, 1998, p. 1159; Wolcott, 1992, p. 19). In cultural analysis observation data typically include internal and external spaces, rooms and buildings captured in photographs, site maps and diagrams (Gottdiener, 1995; Krathwohl, 1998); archival records of media events in newspapers, magazines, film and television (Barthes, 1967/1990; Kress, & van Leeuwen, 1996); and laboratory inscriptions, tables, graphs, charts, and diagrams, in scientific reports and journal articles (Latour, 1987, 1990; Lemke, 1998a). The data sources needed for semiotic analysis of clinical chemistry include disciplinary course materials, observations of practical activities in the laboratory, in instruments, printouts, and reports, and the inscriptions within them. These data sources are described for convenience in terms of the six complementary sources of evidence applied in case study analysis, documentation, archival records, interviews, direct observations, participant- observations, and physical artefacts (Yin, 1994, pp. 79-80). Interviews are not included in this thesis because of the ethical and logistical constraints associated with this form of data. Interviews are not needed in any case, to demonstrate the effectiveness of semiotics, or other forms of cultural analysis that seek subjective experience as it is embedded in history and material culture (Gottdiener, 1995). The first source of data is documentation as provided in clinical chemistry course materials (Appendices A. & B.), scientific journals and textbooks providing analytical and clinical chemistry disciplinary knowledge (e.g. Burtis, & Ashwood, 1999). This source of data is used to provide valid sources of clinical chemistry information in drawing up a structure for clinical chemistry knowledge. In case studies, biased selection of samples from documents constitutes a weakness (Yin, 1994), but in testing theory, purposive samples are selected (Krathwohl, 1998). 164

The second source of data is participant-observation of events. These data are expected to provide insights into motivations and behaviours in particular settings, but are also vulnerable to bias and researcher manipulation (Yin, 1994, pp. 87-89). As discussed in Section 5.3.1, the attempts made by demonstrators to observe and participate in laboratory classes at the same time had to be abandoned, but instrument printouts were retained as evidence of performance. Printouts are incorporated with artefacts below. The third source of data can be drawn from direct observation of activities as they happen in context (Yin, 1994, p. 80). Features of a context such a laboratory can be captured in photographs, maps and schematic representations of the layout of its instruments. Photographs are valid if sources are acknowledged, but they are also subject to bias and manipulation (p. 87). The photographs used in the spatial analysis of the laboratory (explained in Section 5.3.3.1) do not coincide precisely in time or in space (Figure 6.4a) (photograph courtesy of Ron Epping, School of Life Sciences, QUT) with the schematic representation of the instrument laboratory (Figure 6.4b). The instrument laboratory is drawn schematically for the way the instruments were placed in the same year (1998) that students’ practical reports were collected. The schematic representation of a core industry laboratory has been drawn based on site visits to a private pathology laboratory in Brisbane (Figure 6.5c). The photographed laboratory (Figure 6.5a) (photograph courtesy of Alyson Coxon, and with permission from Bruce Campbell at Sullivan and Nicolaides Pathology Services), is represented roughly by the top half of Figure 6.5c. The totally automated laboratory has been redrawn from a clinical chemistry journal article (Figure 6.6) (Boyd, Felder, & Savory, 1996). The Point-of-Care Testing (POCT) laboratory has been adapted from textbook information (Price, & Hicks, 1999) (Figure 6.7b), and is accompanied by a photograph of the specialised glucometer for home glucose monitoring (photograph courtesy of Ron Epping, School of Life Sciences, QUT). These observations of laboratory settings will be used as descriptions, and as illustrations of the way laboratories are transforming (Section 6.3). Their purpose is to derive insights into laboratory experience, and not to specify laboratory organisations precisely. The fourth data source is artefacts which can include art, technologies, tools, instruments and printouts from which insights can be derived into both cultural activities and technical operations (Yin, 1994, p. 90). Bias in the selection of artefacts can be a problem, but not if purposive samples are used for testing theory. 165

Laboratory instruments and printouts, which are fundamental components in clinical chemistry operations, are used to demonstrate semiotic logic in laboratory practice. Those selected are commonly used in experiments (Appendices B. & C.; Figures 7.3, 6.5b, & 6.7a). Finally, archival records provide stable sources of evidence because they are stored and available for re-examination (Yin, 1994, pp. 83-84). Anonymous student practical reports carry the traces of students’ data handling capabilities in graphs and statistics (courtesy of Cyril Craven, School of Life Sciences, QUT). These practical reports provide sources of error used for explaining logic and symbolic analysis in laboratory practice (as explained in data analysis in Section 5.3.3.2). Although there are approximately 900 reports, only certain (purposive) samples of error are selected (e.g. Appendix D.). The reports remain in the archives awaiting responses to other research questions or reliability checks (Lemke, 1998b; Yin, 1994) (Section 9.5). The five data sources listed will be used in various combinations in data analysis to demonstrate the effectiveness of semiotics applied to the structure of clinical chemistry knowledge, and logic and rhetoric in laboratory practice. The next section on data analysis explains how the semiotic framework will be linked with these data sources in response to the research questions.

5.3.3 Data analysis

This section presents an action plan for organising the data sources into codes or thematic categories in order to operationalise the research questions and link data sources with the semiotic framework (see Figure 4.18) (Coffey, & Atkinson, 1996; Krathwohl, 1998; Miles, & Huberman, 1994; Yin, 1994). Some of the data sources, particularly practical reports, require reduction to manageable proportions so that patterns in the data can be matched with the patterns predicted or expected based on theory (Coffey, & Atkinson, 1996, p. 28; Yin, 1994, p. 106). Three semiotic analyses are applied to answer the four research questions, as stated in Section 5.1, and restated with each analysis in the next three sections. The first analysis applies to the structure of clinical chemistry Discourse, addressing disciplinary content derived from clinical chemistry course materials, and the laboratory context as described with the aid of photographs and schematic diagrams. The second analysis applies to logic in laboratory practice, and is made operational by drawing on fragments of the 166 clinical chemistry knowledge base derived in the first analysis, and error scenarios drawn from students’ practical reports. The third analysis applies to extended forms of competence and pragmatics in laboratory practice by expanding on the second analysis, and further textbook information is used to demonstrate rhetoric in the framing of laboratory test information.

5.3.3.1 The structure of clinical chemistry Discourse

There are two components to the first analysis of clinical chemistry Discourse (Chapter 6), addressing the first and second research questions. A response to the first question: What structure of clinical chemistry knowledge applies in automated computerised laboratories in the era of socially accountable laboratory medicine? is given in Section 6.2. The data used in this analysis are drawn from clinical chemistry course materials, textbooks, and journals. Semiotics cannot describe the whole of clinical chemistry knowledge, only fragments of that knowledge. The substantive structure of clinical chemistry disciplinary knowledge can however be represented in terms of its relations with other forms of knowledge pertaining to the cultural phenomenon Health, under the cultural sign model derived from structural linguistics in Section 4.2 (Figures 4.4 & 6.1). Additional guidance is sought from the structure of scientific disciplines (Schwab, 1962, 1964a, 1964b), and the archaeology of knowledge applied to clinical medicine (Foucault, 1969/1972, 1963/1973), to assist in defining the substantive structure of contemporary clinical chemistry knowledge. The focus of attention is directed towards verification of analytical procedures, with respect to tests, chemical analysis systems, methods, data handling, quality control and clinical interpretation, based on course and textbook information, summarised in the laboratory test loop (Figures 6.2 & 6.3), and is guided by clinical chemistry course requirements (Appendices A. & B.). A response to the second research question: What contextual factors constrain knowledge work in automated computerised laboratories? is given in Section 6.3 in two ways. Firstly, the teaching laboratory is described and compared with industry laboratories in terms of spatial arrangements, illustrated with photographs (Figures 6.4a, 6.5a, 6.5b, & 6.7a) and schematic diagrams (Figures 6.4b, 6.5c, 6.6, & 6.7b) (Sections 6.3.1 & 6.3.2). The laboratory is analysed around the variant “space”, in the plane of expression, which provides the organising principle for description (Sections 4.2.3.2 & 6.3.1), and 167 comparison with industry laboratories (Section 6.3.2). These descriptions and comparisons are used as points of departure in connotative analysis (Section 4.2.3.3, 4.2.4.2, & 6.3.4). The “rhetoric” of laboratory instrument designs (form and substance of the expression), and the articulation of material forms and technical ideology (form of the content), are examined in order to gain insights into the constraints on knowledge work in industry laboratories. The analysis demonstrates the flexibility of semiology, structured denotations and unstructured connotations applied to spaces and objects (Figure 4.18).

5.3.3.2 The logic of clinical chemistry laboratory practice

This section explains the organisation of data used in Chapter 7 in response to the third research question: What modes of reasoning do knowledge workers apply and what adds value to clinical chemistry laboratory practice? Three broad sections are needed to demonstrate the semiotic basis of logic. Three modes of reasoning, induction, deduction, and abduction, are assigned respectively to the description and classification of instruments, their ideal rule-governed use, and detection and diagnosis of errors (as explained in Section 4.3.3). The first mode of reasoning, induction, is assigned roughly to the description, classification and selection of instruments (Section 7.2). The structuralist principles, division, classification and system, are demonstrated by drawing data samples from clinical chemistry course materials (Appendices A. & B.), textbooks (Burtis, & Ashwood, 1999; Holme, & Peck, 1998; Meloan, 1968a; Vogel, 1961), and laboratory instruments. From the classification of chemical analysis systems (Figure 7.1), one system, namely spectrophotometry, is selected for specific treatment (Section 7.2.2), based on the frequency of its application in clinical chemistry courses. Instruments called spectrophotometers are used in 24 out of 26 practical sessions (as listed in Appendix B1.), and the particular form, the molecular absorption spectrophotometer (MAS), is used in all but three practical sessions. This means that all of the data acquired from practical classes, instrument printouts and practical reports are associated with MAS use (Appendices B. & C.) The spectrophotometer is subjected to structural analysis in the plane of expression (Section 7.2.2), described in the expression line (Section 7.2.2.1) and compared with similar but different instruments in the expression side (Section 7.2.2.2). The correlation of the plane of expression 168 with a plane of content in a semantic fragment (Figure 7.6 derived from Figure 4.14), is used to demonstrate the way analysts make choices in different analytical circumstances, considering theory, technology, and pragmatic factors such as availability of parts and services, staff, space and budgets. The significant points or points of invariance (POV) isolated in the expression line of MAS are the points at which rule-governed logic and error detection and diagnosis can be observed in the second and third aspects of analysis. The second mode of reasoning, deduction, is assigned to the ideal rule- governed use of MAS based on the Beer-Lambert Law that circumscribes a valid MAS experiment (Section 7.3.1), and data manipulation and calculation (Section 7.3.2). This demonstration draws on textbook information (e.g. Holme, & Peck, 1998) to explain what can be expected in a MAS experimental situation (Figure 7.8), and how the data produced in experiments are manipulated in tables of data, graphs, charts and statistics (e.g. Appendices B3. & B4.). The third mode of reasoning, abduction/hypothesis, is demonstrated in detection, diagnosis and troubleshooting errors (Section 7.3.3). In seeking to demonstrate data and results validity (Figure 6.3, Levels 2-5), a knowledge worker or symbolic analyst can recognise symptoms and clues of error in the experimental situation, in the use of instruments, and in the data handling stages of experiments (graphs, charts and statistics). By interpretation of signs of error, the analyst, abducting the rule, law or principle that governs the experiment, can diagnose their probable causes. Semiotic logic is demonstrated at the POVs, the points from which significance emerges, identified following MAS analysis in the plane of expression in Section 7.2.2. There are three sources of data used to demonstrate how error detection and diagnosis occurs. Instrument printouts capture errors while instruments are being used (Appendix C.); students’ practical reports capture errors in graphical and statistical data manipulations (e.g. Appendix D.); and quality monitoring charts drawn from textbook information capture errors that accumulate in analytical systems over time (Westgard, & Klee, 1999). Only a few purposive samples are provided in the instrument printouts and textbook examples, but it was necessary to reduce the large number of student practical reports down to manageable proportions, in order to extract a few examples of common error occurrences. The signs of error provided directly by instrument windows and printouts are given in the instrument response, absorbance reading, as high, low, unstable, and 169 negative readings (Section 7.3.3.1) (Figure 7.8, POV 5), some of which are recorded in instrument printouts (Appendix C.). Errors can be detected and diagnosed after the experiment in data manipulations in graphs, charts and statistics (Section 7.3.3.2). Approximately 900 practical reports, collected originally with the purpose of assessing students’ ability to handle scientific representations, were analysed and reduced to six error scenarios in graphical and statistical handling of data. This reduction process was achieved by analysing the comments made on students’ practical reports, marked in red pen by demonstrators (e.g. Appendix B5.). The comments were organised into categories of error, professional and technical issues such as presentation and legibility; conceptual issues with MAS calculations, and errors in the interpretations of graphs and statistics; and clinical interpretations (e.g. Appendix D.). Six striking error scenarios (random error, misunderstanding, non- linearity, loss of linearity above a certain point, intercept error, and outlier) were extracted in this process (illustrated graphically in Appendix D.), to demonstrate that semiotic logic applies to the detection of clues in data, graphs, and statistics, that experimental error has occurred. Clues are similarly hidden in QC charts from which inferences may be drawn about the nature of analytical errors occurring over time (Section 7.3.3.3). In troubleshooting laboratory errors, it is demonstrated in each case that multi-literacies are used.

5.3.3.3 The rhetoric of laboratory testing

This section addresses the analysis of data used in response to the fourth research question in Chapter 8: What range of competencies do knowledge workers apply in clinical chemistry, and what additional skills will add value in socially accountable laboratory medicine? The analysis draws on the outcomes of the previous analyses as data sources in order to derive the full range of competencies, from “d” operational competence in clinical chemistry laboratory practice, to “D” competence in medical science Discourse for the purposes of EBLM (Figure 8.1). The semantic fragment of MAS navigated for instrument selection (Figure 7.6) is expanded to incorporate selections based on pragmatic managerial concerns (Figure 8.2); and further expanded to highlight opposing interpretive choices in evaluations of laboratory test information, based on values, biases and vested interests (Figure 8.6). Rhetoric in the framing of laboratory test information is demonstrated by 170 drawing a sample from a published laboratory test evaluation (Shultz, 1999). The laboratory test, renamed serum “X” in order to avoid misrepresentation of the actual test, is evaluated using different graphical and statistical representations. The art of rhetoric, or persuasion, is demonstrated in the framing of laboratory test information for its communication to the clients of pathology services. This dimension of symbolic analysis, the recognition of the values, ideologies, and vested interests connoted in scientific representations, will add value in EBLM (Muir-Gray, 1997).

5.4 Reliability and validity issues addressed

Terms such as “trustworthiness”, “credibility”, and “data dependability” are sometimes applied in evaluations of qualitative research (Denzin, & Lincoln, 2000; Marshall, & Rossman, 1999). Validation is sought internally, in the demonstration of links between theory, evidence and research questions; externally in the representativeness of samples, and the potential for generalisation of findings; and dependability of data sources and data collection techniques is sought in the replicability of findings (Creswell, 1998; Krathwohl, 1998; Marshall, & Rossman, 1999; Yin, 1994). There is debate about which criteria apply in particular cases, because some forms of qualitative research produce unique results and their value lies in the insights they provide into complex social issues (Creswell, 1998; Silverman, 2000). There are nonetheless quality issues that are readily addressed with respect to data sources, data collection techniques and data analysis. Individually, data sources have weaknesses and therefore the collection of multiple data sources is generally considered to strengthen qualitative research (Creswell, 1998; Krathwohl, 1998; Miles, & Huberman, 1994; Yin, 1994). Data collection techniques introduce ethical issues, access and other logistical problems which place limitations on the research (Krathwohl, 1998; Yin, 1998). Some qualitative researchers recommend triangulation of data, methods and observers, to demonstrate convergence in the interpretations (Krathwohl, 1998; Miles, & Huberman, 1994; Yin, 1994). Others argue that qualitative research problems are too complex to expect these kinds of convergences (Silverman, 2000). In data analysis, it is expected that samples drawn from the unstructured base of field notes and other data sources will provide a fair representation of the whole data set, and are thus externally valid (Krathwohl, 1998; Yin, 1994). It is less of an 171 issue however if non-probability samples are selected purposively to demonstrate the logic of theory (Coffey, & Atkinson, 1996; Krathwohl, 1998). Such research however, will have to demonstrate internal validity by linking data sources logically with theory in responding to the research questions (Krathwohl, 1998; Yin, 1998). There are forms of qualitative research that are generalisable to theoretical propositions, but are not replicable in other situations because they are unique. Such research can be made “operational” and replicable by rigorous attention to data management and accessibility (Yin, 1994, pp. 36-37). This is essential in order that the data can be re-analysed from different perspectives posing different questions, to clarify ownership of data, and to avoid fraud and misrepresentation (Krathwohl, 1998; Lemke, 1998b; Yin, 1994). This chapter demonstrates that multiple data sources have been accessed, and ethical concerns have been addressed. Data analysis demonstrates the links between data sources and semiotic theory addressed to the research questions; and the research process has been made operational by making archival records accessible to other researchers, either to replicate findings, or for use as evidence in other lines of inquiry. In the final analysis, the credibility of the research rests with peers in the higher education sector, in clinical chemistry, and in semiotics, and whether they are persuaded that semiotics provides a powerful tool that can be applied to clinical chemistry knowledge and practice in the era of socially accountable laboratory medicine (addressed further in Section 9.4).

5.5 Conclusion

In summary, this chapter has considered approaches to cultural analysis, data sources, data collection techniques and data analysis, and the semiotic analysis of various sources applied to clinical chemistry knowledge, context and practice. The first analysis in Chapter 6 addresses the structure of clinical chemistry knowledge and the constraints of the laboratory context on laboratory practice. 172

Chapter 6 The structure of clinical chemistry Discourse

6.1 Introduction

There are many ways to structure a discipline as complex as clinical chemistry, and many kinds of laboratory organisation. Experts explain the scientific, physical and biological science content of clinical chemistry, and the pragmatic concerns of laboratory organisation and management (e.g. Burtis, & Ashwood, 1999), but there is no easy way to structure clinical chemistry Discourse. Its systematic basis is difficult to discern under the huge volume of information provided in textbooks. The purpose of this chapter is to apply semiology to medical science culture in two ways, addressing the first and second research questions (Section 1.4). Firstly, in response to the first research question, semiology is applied to the structure of clinical chemistry Discourse, also understood as a transdisciplinary (Mode 2) knowledge system, integrating scientific, managerial, economic and political considerations, with the wider concerns of Evidence-Based Laboratory Medicine (EBLM). Secondly, in response to the second research question, semiology is applied to the context of laboratory practice, including the tools or instruments of chemical measurement. Both aspects of clinical chemistry Discourse, knowledge structure and context, are important when socio-cultural perspectives of knowledge work in automated computerised laboratories are considered (as explained in Section 3.5.1). Whereas semiology makes use of structuralist principles as a means for describing and classifying objects, its purpose is not to describe per se, but to gain insights into the unstructured subjective world of consumer experience through connotative analysis of spaces and objects (refer to Sections 4.2.3.3 & 4.2.4.2). Objective analysis is used to provide the points of departure for connotative analysis, from which further insights are gained into subjective laboratory experience, and the potential for de-skilling and displacement of medical scientists by automation, robotics and informatics (as discussed in Section 2.4). Insights are also sought into socio-ideological aspects of medical science Discourse, and additional skills in social criticism needed for clinical chemistry test evaluations in EBLM. The data sources 173 used for these purposes are drawn from observations of teaching laboratories, industry laboratories, and clinical chemistry course materials (as detailed in Section 5.3.2). This analysis is biased towards the academic perspective, and although observations are drawn from real world laboratory situations, no claims are made that this analysis would substitute for an expert clinical chemists’ analysis of laboratory designs. That task is left for laboratory managers (e.g. AACB, 1998b, 1999a, 2001). The samplings of clinical chemistry knowledge content are derived purposively from textbooks and course materials, and laboratory designs are restricted to those represented in textbooks, newsletters and journals, or those to which access has been given. It is assumed for the purpose of spatial analysis, that the teaching laboratory and industry laboratory designs used are fairly representative of other teaching laboratories and other industry laboratories. There are three stages of clinical chemistry Discourse analysis. Firstly, clinical chemistry, a core discipline of medical laboratory science, is located within the medical science field of clinical pathology, a principle formation of Western medicine (Section 6.2). The analysis is guided by the cultural sign model (Figure 4.4); and is supported by Foucault’s archaeological approach to clinical medicine (Foucault, 1969/1972; 1963/1973); and Schwab’s more specific outline of the structure of scientific disciplines (1962, 1964a, 1964b). The aim of this first stage of analysis is to derive a summary of the principle activities used by clinical chemists to validate laboratory data, results and tests (Figures 6.2 & 6.3). This summary will be used in Section 7.3 in the demonstration of semiotic logic applied in laboratory practice. Secondly, the clinical chemistry laboratory context is analysed as a system of objects, at the denotative level, in the plane of expression, in order to describe it and compare it with industry laboratories (Sections 6.3.1 & 6.3.2) (as explained in Sections 4.2.2 & 4.2.3.2). Thirdly, connotations are superimposed on objective descriptions of laboratory spatial arrangements and instrument designs, in order to gain insights into socio-ideological aspects of laboratory practice and experience (in Section 6.3.4) (exemplified in Section 4.2.3.3).

6.2 A structure of clinical chemistry Discourse

There is no established way to structure clinical chemistry understood as Discourse. Clinical chemistry textbooks (e.g. Burtis & Ashwood, 1999) order topics 174 into logical categories along similar lines to the list of categories recorded in the Australasian Association of Clinical Biochemists (AACB) Board of Examiners Syllabus (AACB, n. d.; Appendix A.). The topics dealt with in these data sources indicate that clinical chemistry is an applied science discipline incorporating the knowledge bases of other disciplines, physics, chemistry and biology, and mathematics; and that laboratory practices are supervised by laboratory managers, and are regulated by technological, political and economic factors. In these data sources, a systematic basis or structure of clinical chemistry Discourse, understood as a transdisciplinary Mode 2 knowledge system, is not readily discerned, a structure that is, that integrates scientific and non-scientific aspects simultaneously. The purpose of this section is to demonstrate how semiology applies to the structure of clinical chemistry Discourse. A systematic basis of clinical chemistry knowledge will account for scientific criteria in data and results validation; pragmatic considerations in quality monitoring and quality assurance; and political, legal, economic and social considerations for the purposes of EBLM. This structure provides the reference points for demonstrating the way semiotic logic and pragmatics apply to knowledge work and symbolic analysis in laboratory practice in Chapters 7 and 8. Clinical chemistry is first located as a core discipline of clinical pathology, a “form of the content” of Western medicine.

6.2.1 The “substance of the content”: Western medicine

The analysis of Discourse can be considered from many perspectives. Three approaches, semiology (Barthes, 1964/1973; Hjelmslev, 1943/1961), archaeology (Foucault, 1969/1972), and the structure of scientific disciplines (Schwab, 1962, 1964a, 1964b) are considered in this section. This is because semiology provides an overarching abstract model that is adaptable to all cultural phenomena (Figure 4.4); archaeology is applied specifically to clinical medicine; and the structure of scientific disciplines is equally applicable to applied science disciplines. The juxtaposition of these three approaches helps clarify structuralist processes applied to disciplines and Discourses. Note however that Foucault’s method is not strictly structuralist, and there are many points of commonality and difference between structuralism and archaeology (Foucault, 1969/1972, pp. 199-201). Hjelmslev (1943/1961) provides a language model for analysing culture (Section 4.2.2) summarised in the cultural sign 175 model (Figure 4.4), incorporating the internal, systematic aspects of language, or grammar, and external factors such as political, legal, economic, geographical, psychological, and social issues. Ultimately everything that can be known of a phenomenon can be accessed through this structure of language. However the factors external to language, or grammar, are accessible only by a relatively unstructured analysis, by a creative analyst performing connotative analysis (Barthes, 1967/1990), and by operating with pertinent fragments of knowledge (Eco, 1976). The archaeological approach to Discourse analysis applied by Foucault (1963/1973) to clinical medicine at the turn of the nineteenth century is equally applicable to contemporary medical laboratory science. Foucault (1969/1972) refers to the interrelations between the statements that make up complex Discourses such as clinical medicine as “discursive formations” (p. 31). This is because clinical medicine although “organised as a series of descriptive statements”, was also a “group of hypotheses about life and death, of ethical choices, of therapeutic decisions, of institutional regulations, of teaching models” so that descriptive statements were only one aspect of medical Discourse (p. 33) (note that Capital “D” Discourse is retained following Gee, 1996). Medical Discourse relies on knowledge from scientific disciplines as well as knowledge from a number of disparate fields. If there is unity or structure in medicine Foucault argues, it will be found not only in propositions or statements or architectonic science components, but also in external factors, cultural influences and traditions (p. 34). Clinical medicine requires a heterogeneous set of statements and practices, incorporating perceptual descriptions, instrument mediated observations, experiments, and consideration of patient demographics, epidemiology, institutional regulations and therapeutic practice (p. 34). This description applies equally to EBLM. The theoretical Discourse category, discursive formation, is roughly equivalent to the more recent concept, transdisciplinary Mode 2 knowledge system (Gibbons, Limoges, Nowotny, Schwartzman, Scott, & Trow, 1994). Recapitulating on the discussion in Section 3.2, Mode 1 knowledge systems are autonomous science disciplines, for example, physics, chemistry, biology and geology, defined by internal rules and procedures. The activities of single disciplines are insufficient for solving complex real world problems, transdisciplinary Mode 2 knowledge systems are needed, involving collaborations between disparate fields, and accountable in economic and social terms. The Mode 1/Mode 2 knowledge distinction also has a 176 counterpart in the distinction drawn by Schwab (1964b) for the structure of scientific disciplines, between the “short term syntax” of stable inquiry (p. 31), understood as architectonic (Figure 4.18 below heavy line), and the “long term syntax” that renders stable inquiry problematic (Figure 4.18, above heavy line), revealing their inherent contradictions, and their ongoing evaluation and revision (p. 39). The expressions “discursive formation” and “Mode 2 knowledge system” can be roughly equated and applied to the medical sciences and clinical chemistry. Clinical chemistry is a Mode 2 knowledge system, because it is composed of more than an aggregation of knowledge derived from single scientific disciplines, each with a set of validated propositions and statements. Disciplinary Mode 1 knowledge is derived in science disciplines such as physics, chemistry and biology, whose activities are relatively autonomous and “architectonic” because they are drawn from specific conceptual frameworks, defined by internal rules of coherence and specific validation criteria (Balzer, Moulines, & Sneed, 1987). The values of scientific Mode 1 forms of knowledge are determined by the success of their explanations, predictions and technological applications (Moulines, 1996, p. 8). In scientific disciplines, external factors such as political and ethical dimensions have in the past been excluded as much as possible (p. 2). In addition to scientific knowledge, clinical chemistry Mode 2 Discourse is also structured according to technological developments, budgets, political constraints such as Medicare reimbursement schedules, legal constraints, and other social and ethical issues of concern in EBLM (AACB, 2002a, 2002b; Farrance, 2000; Price, 2001). In this section clinical chemistry Discourse is located within the medical science field as a formation of the unstructured phenomenon, Health (Figure 6.1). The clinical chemistry laboratory environment is understood as a material expression (form and substance of the expression) of the substantive content Western medicine, and its formation, pathology testing (form of the content). According to structuralist principles, things acquire meaning by their relations with similar but different things. The Health continuum can be ordered to different “substances” with health goals in common, but with different health perspectives from Western medicine (e.g. Chinese, Homeopathic, Herbal, and Natural medicines). Western medicine is itself formed in a number of ways, each of these ways having distinctive substantive contents and conceptual structures, and each in turn being formed in a number of ways. The Health formation, pathology testing, represents an ideological stance 177 about health based on interventionist perspectives of medicine, along with radiology (diagnostic imaging) and pharmacology (drugs). There is however a conceptual opposition to these interventionist approaches, in social and preventive medicine.

Phenomenon Health

Perspectives in Health: Western ≠ Chinese ≠ Natural Medicine

S Interventionist ≠ social & preventive C F Pathology ≠ Radiology ≠ Pharmacology E F Spatial arrangements, object morphology S Technology, laboratory, objects

Clinical Chemistry Microbiology Immunology Histology Haematology Cytology

Purport/Matter/Continuum

Figure 6.1. A cultural sign model of Health.

The pathology testing formation of medicine is in turn formed in a variety of ways, and pathology (form of the content) materialises in technological applications in the laboratory (substance of the expression), and laboratories in turn have different formations including clinical chemistry, immunology, haematology, microbiology, histology, cytology, and molecular diagnostics (forms of expression) (as explained in Section 2.2). Each pathology discipline has its substantive content and conceptual structure (forms of content), and each makes use of particular techniques and instruments of analysis. Laboratory instruments also have structure and can be subjected to analysis in the plane of expression to isolate the points of invariance at which the logic of practice can be observed (as demonstrated in Section 7.2.2). This chapter is concerned with the laboratory context, so that the laboratory is subjected to spatial analysis, ordered in the plane of expression at the denotative level, and unstructured analysis at the connotative level (Section 6.3). The educational significance of locating clinical chemistry in this manner under a cultural sign model of Western medicine is revealed in the connotative analysis (Section 6.3.4). Whereas 178 detailed discussions of Western medicine and comparative analysis of alternative approaches to medicine are beyond the scope of this chapter, ideology criticism is considered in the connotative analysis because it is a fundamental requirement of Mode 2 knowledge systems and therefore EBLM.

6.2.2 A structure for clinical chemistry laboratory practice

This section is divided into general and specific issues. Firstly, an outline of clinical chemistry Mode 2 knowledge applicable to EBLM is given. Secondly, clinical chemistry laboratory practice is reduced to a core set of validation criteria, for use in demonstrating the logic of laboratory practice in Section 7.3

6.2.2.1 Clinical chemistry transdisciplinary Mode 2 knowledge

Clinical chemistry is defined as “the application of chemical, molecular, and cellular concepts and techniques to the understanding and evaluation of human health and disease” (Athena Society, 1996, p. 99). Towards this specific purpose, several kinds of theory are applied, from the biological sciences, anatomy, physiology and biochemistry, for clinical interpretation of results; from the physical sciences, physics and chemistry, with respect to instrumentation and chemical methods; and mathematics and statistics are used in the calculation and evaluation of data and results (Appendix A.). This definition does not however, give a complete picture of clinical chemistry, but reflects the scientific basis of clinical chemistry knowledge. It fails to account for the way Mode 1 knowledge systems or disciplines are being subsumed within transdisciplinary Mode 2 knowledge systems requiring collaborations between disparate fields, so that economic viability and social accountability in pathology services is ensured (Gibbons et al., 1994; Price, 2001). There is a wide distribution of clinical chemistry knowledge as for clinical medicine and other technical professions, at technological, economic and management levels. Clinical chemistry knowledge is becoming even more widely distributed as the pathology industry is required to demonstrate evidence that laboratory tests are cost- effective, clinically relevant and appropriate to use (Muir-Gray, 1997; Price, 2001). Clinical chemistry knowledge, and the medical laboratory sciences in general, is transdisciplinary Mode 2 knowledge, architectonic, based on laws, principles and 179 rules of procedure borrowed from physics, chemistry, mathematics and biology, and dispersed throughout many pragmatic domains, technical, managerial, political, economic, legal and social. Clinical chemistry can be subjected to analysis at all three levels described by semiology, structure, statement, and connotation (Figure 4.18). At the first denotative level, objects and theories are analysed in the plane of expression (the comparative analysis of forms) and the plane of content (the interrelations between theories) respectively (Figure 4.18, Level 1). As explained in Section 4.3.5, it is impossible to give structure to global semantic systems, so that structured analysis of clinical chemistry knowledge can best be considered by isolating pertinent fragments for particular purposes (as demonstrated in Section 7.2.2). The second level of analysis is concerned with scientific statements, produced once systematic explanations of phenomena are proposed or hypothesised, usually formalised mathematically, and subjected to verification in experiments, in the short term syntax of stable inquiry of scientific disciplines (Schwab, 1964b) (Figure 4.18, level II). Experiments in clinical chemistry are based on scientific statements and their assumptions, but their applications require validation. This is demonstrated in Section 7.2.2, for physical theories about electromagnetic radiation (EMR), because they have a wide range of applications in laboratory measurements. The second level described by semiology, the meta-semiotic level, is crucial for outlining a systematic structure for clinical chemistry. Reliability and validity criteria are used to determine the validity of scientific statements, rules, laws and principles, and thereby confer on scientific knowledge its revisionary character, leading to the development of concurrent theories for a given subject matter (Schwab, 1964b, pp. 27-28). It is not the concern of clinical chemistry, an applied science discipline, to challenge rules, laws and principles, but to test the reliability and validity of their applications. Practically every aspect of clinical chemistry knowledge is built around reliability (precision or repeatability) and validity (accuracy or closeness to “true” result) criteria (Kringle, & Bogovich, 1999; Westgard, & Klee, 1999). A systematic structure of clinical chemistry knowledge will thus be based around reliability and validity criteria, with respect to the data produced in the immediate context of experiments, to the quality of results over time, and to the reliability and validity of laboratory tests to accurately diagnose the presence or absence of disease (Fraser, 180

2001; Shultz, 1999). Approaches to validation in clinical chemistry are given detailed treatment in Section 6.2.2.2. The third level of analysis will extend the set of structured and scientific validation criteria for laboratory data and results, into the unstructured domain of pragmatics (Figure 4.18, above heavy line). In the evaluation and selection of chemical methods and instruments, clinical chemistry knowledge is dispersed throughout many domains, scientific disciplines, technology, and management of space, time, staff and budgets (AACB, 1998b, 2001; Weiss, & Ash, 1999). More than understanding of laboratory practice is required from connotative analysis. Insights into socio-ideological aspects of laboratory practice are sought, in the search for evidence that laboratory tests are valid; and understanding of the constraints imposed by laboratory Discourse on the knowledge work experience (Section 6.3.4).

6.2.2.2 Validation in clinical chemistry laboratory practice

In this section, a baseline of clinical chemistry laboratory activities is derived from the content base as indicated in the AACB course outline (Appendix A.) and the university unit outlines (Appendix B1.). There will however be many ways to organise clinical chemistry knowledge, depending on point of view and purpose. The structure provided is based on the reliability and validity of the applications of scientific rules, laws and principles used in clinical chemistry in the acquisition of data and results from biological specimens; their quality evaluations; and clinical interpretations; and some of the pragmatic concerns of laboratory management. These validation criteria are used in this section to provide a systematic basis for clinical chemistry knowledge, and the range of validation activities is represented in the “laboratory test loop” to address the validity of laboratory data, results and tests (note that the term “laboratory test loop” is borrowed from, but not represented by Lundberg, 1998) (Figure 6.2). Postgraduate studies in clinical chemistry are generally undertaken as part time studies in conjunction with employment in the pathology industry. Continuing Professional Development (CPE) is commonly conducted through membership (and fellowship) examination (Appendix A.), which also provides the model for undergraduate studies in clinical chemistry (Appendix B.). To acquire the credential MAACB (Member of the AACB), a clinical biochemist must acquire knowledge and expertise in a wide range of techniques and 181 procedures, broadly classified under three headings, “Analytical Biochemistry”, “Clinical Biochemistry” and “Laboratory Management” (Appendix A.).

Specimen Laboratory Test 1

Biology Clinical Validation Interpretation 6 2 Measurement System

Rationalistic principles & mathematical rules

Results Chemical method Quality Assurance 5 3

Data handling

4 Statistical probability Pragmatics

Figure 6.2. The laboratory test loop.

In order to attain the requisite knowledge of Analytical Biochemistry, a clinical biochemist will become acquainted with “the theoretical principles and techniques underlying the full range of clinical biochemical analyses”. Particular emphasis is placed on “the factors which govern the choice of method and on the evaluation of instruments and methods” (Appendix A.). This means that the physico- chemical principles of methods, instruments, and techniques must be understood at a level sufficient to allow operators to discuss classes of instruments and their relative merits, and be able to select the appropriate mode of analysis in different analytical situations (as demonstrated in Section 7.2.2.2). The clinical biochemist is expected to be able to assess the validity of measurement systems and chemical methods, a requirement represented under the headings “Laboratory Data Processing and Computing” and “Analysis of Laboratory Error/Statistics”. In the analytical biochemistry category, the validity of data is based on the validity of measurement systems and chemical methods, and the quality of results is determined for reliability and validity, or precision and accuracy, using statistical techniques (Figure 6.2, Levels 2-5). 182

Once data and results are accepted as valid, clinical biochemists turn their attention to the clinical interpretation of results. Under the heading “Clinical Biochemistry”, the clinical biochemist is required to understand the principles of biochemical and physiological functions in the diagnosis and monitoring of disease processes (Appendix A.). Knowledge of anatomy, physiology and biochemistry is required for the “interpretative aspects of clinical biochemistry”. For example, knowledge and understanding of the functioning of organs, liver, kidney, brain, heart, and intestine; the metabolic pathways for carbohydrates, fats, proteins, vitamins and minerals; the catalysis of those functions by enzymes (Diagnostic enzymology); and the facilitation of those functions by hormones (Endocrinology). The clinical biochemist is also expected to interpret drug effects in Therapeutic Drug Monitoring (TDM), and to understand their pharmacology and toxicities. This aspect of the curriculum aims to equip clinical biochemists with the knowledge needed to consult with clinicians about test performance. Understanding is also required of the variables, geographic, age, sex, environmental, seasonal, and dietary and lifestyle factors that influence laboratory test results (Fraser, 2001) (Figure 6.2, Level 6). Under the heading “Laboratory Management” (Appendix A.), the clinical biochemist is required to be conversant with a wide range of organisational issues including spatial organisation, laboratory design, instrument acquisition, methods, quality assurance, staffing arrangements, budgets and safety. Pragmatic factors are thus added to scientific and technical concerns, and are placed under the subheading “Quality Management Systems” (QM). QM encompasses all the managerial issues needed to accomplish reliability and validity of data and results, and to ensure quality and the validity of clinical interpretations. The laboratory test loop however is incomplete, unless the laboratory test itself is valid to use, because even if data and results are valid, the test itself may not be clinically relevant, appropriate or cost effective (Figure 6.2, Level 1). This level of validity is the concern of EBLM (Morris, 2000; Price, 2001). Undergraduate teaching of clinical biochemistry tends to follow the AACB model, although less comprehensively, particularly in the Laboratory Management aspects, which are restricted to issues of safety and analytical Quality Control (QC) (Appendix B.). Figure 6.3 adds detail to the validation levels identified in Figure 6.2, in terms of laws and principles for measurement systems and chemical methods, calculations, quality assurance, and clinical interpretations. 183

PATHOLOGY PRINCIPLES GRAPHIC/NUMERIC VALIDITY PRACTICE REPRESENTATIONS

SPECIMEN SPECIMEN

Test selection & Biological principles & Test evaluation: LEVEL 1 evaluation social, legal, political, sensitivity & specificity of Test economic & other tests, ROC curve. factors Analytical Physical principles: Instrument evaluation: LEVEL 2 instrument laws, theories, concepts sensitivity, accuracy & System selection, precision studies calibration & performance evaluation

Method selection Chemical basis of Method evaluation: LEVEL 3 & performance methods specificity, accuracy & Method evaluation precision studies

Data handling Mathematical & Data reduction & LEVEL 4 statistical rules transformation, curve- Data fitting, linear & non-linear regression analysis, interpolation & calculation Quality Control Statistical rules & Quality evaluation in real LEVEL 5 & Quality managerial principles time - precision & Results Assurance accuracy studies of QC materials; Quality evaluation over time using QC charts and external proficiency testing

Clinical Biological principles: Comparison of results LEVEL 6 interpretation anatomy, physiology & with reference Interpretation biochemistry populations. Intra & inter biological variation; diagnostic coherence between tests

Figure 6.3. Validation in clinical chemistry.

The details provided reflect the requirements of the university practical course (e.g. Appendix B.). Five out of six levels of validity (Figure 6.3, Levels 2-5) are addressed in the demonstration of semiotic logic in Section 7.3, because they apply to the use of instruments, chemical methods, calculations, QC, and clinical interpretations. Figure 6.3 captures the multiple scientific activities integrated in a simple pathology request. At levels of validity two and three, laws, principles, and 184 concepts from physics and chemistry are integrated in the performance of experiments and the use of instruments. At the fourth level, data from instruments are reduced to manageable formats in tables and transformed mathematically and graphically, each form providing different information. At the fifth level, the quality of results is assessed and monitored, and at the sixth level, clinical interpretations are made based on biological principles. Each step in the evaluation of data and results is conducted with the aid of representations, graphs, charts, and statistics. The clinical chemist demonstrates operational and cultural competence by ensuring that instruments and methods are functioning optimally, data and results are valid, analytical systems are “in control”, and by making the appropriate clinical interpretations. In the context of work however, it is possible that the theoretical consideration for Levels 2 and 3 validity, instruments and methods, Beer’s Law for example (as explained in Section 7.2), may be taken for granted because methods are “fixed” in manuals of procedures, as a requirement of QM and laboratory accreditation (AACB, 1999b; 2000b). Further to this, many method and instrument troubleshooting diagnostics, clinical interpretations and decision-making processes are increasingly being performed by Expert Systems (Sikaris, 2001). In this situation there is the danger that some medical scientists will be doing the work of routine operators or instrument minders. This danger can only be investigated by a comprehensive analysis of work. For the purposes of this thesis, it is argued that it is a crucial function of medical science education to emphasise the interpretative function of medical scientists (Barley, & Orr, 1997), and to cultivate knowledge workers and symbolic analysts who can perform at least the activities of expert scientists that Expert Systems are designed to mimic (Chi, Glaser, & Farr, 1988; Gillies, 1996; Sikaris, 2001). The representation of clinical chemistry Discourse at six levels for validation constitutes a major simplification of the complexity of clinical chemistry knowledge and practice. The exercise is useful for circumscribing the boundaries of clinical chemistry knowledge at the analytical level at which students learn experimental procedures. The assumption is made that the specimens from patients being tested are valid, but there are many sources or error (pre-analytical) in specimen collection, transport and storage, which also require monitoring (Fraser, 2001; Westgard, & Klee, 1999). At the undergraduate level, the assumption is also made that the laboratory test is valid (Figure 6.3, Level 1), and EBLM being relatively new, is not 185 well addressed in the clinical chemistry curriculum (Morris, 2000; Price, 2001). This leaves medical science Discourse unchallenged, at least at the undergraduate level, and provides subject matter for the connotations of laboratory objects in Section 6.3.4, and Chapter 8. To take the structure of clinical chemistry knowledge any further it is necessary to work with specific fragments (as demonstrated in Section 7.2.2). The rest of this chapter provides an analysis of the teaching laboratory spatial arrangements in order to gain insights into socio-ideological components of laboratory practice and experience in connotative analysis.

6.3 The laboratory as a system of objects

Two broad aspects of semiology are applied in this section. Firstly, the teaching laboratory is ordered according to its spatial arrangements, in the plane of expression (refer to Sections 4.2.2 & 4.2.3.2). No details of analysis are given however, it is simply a matter of taking advantage of the organising principle of similarity and difference, or “conceptual opposition” to facilitate laboratory description (refer to Section 4.2.1). The purpose is not to provide in-depth analysis of laboratory organisations, which is a matter for laboratory managers, but to describe the laboratory, and to compare it with industry laboratories. The purpose of description is to place the second aspect of analysis, connotative analysis, in context, in order to gain insights into the nature of laboratory Discourse, and the constraints it imposes on knowledge work experience. This aspect of the analysis is conducted by exploring the articulation between the material forms of laboratory instruments and the ideology of laboratory practice (refer to Sections 4.2.3.3 & 4.2.4). Laboratories are described and compared in Sections 6.3.1 and 6.3.2, and the “connotations” of laboratory objects are explored in Section 6.3.4.

6.3.1 Spatial analysis of the teaching laboratory

Spatial analysis of the teaching laboratory is conducted in this section with supporting data drawn from direct observations of laboratory layouts, or layouts derived from journal articles, supported by photographs in some instances. Schematic diagrams are used to complement photographs that do not capture the details of large-scale laboratory layouts, and actual laboratory design specifications 186 have not been made available for this analysis (refer to Section 5.3.2). The teaching laboratory (Figure 6.4a) and the schematic layout of the instrument laboratory (Figure 6.4b) were observed in the same period that data were collected of students’ practical work providing illustrations of laboratory error (In 1998). Industry laboratory layouts (Figures 6.5a, 6.5b & 6.5c) used for comparative analysis (Section 6.3.2), have been based on site visits as part of the requirements of university teaching and participation in continuing professional education (CPE), or have been adapted (redrawn, not photocopied) from clinical chemistry journal articles (Figure 6.6). These data sources represent purposive samples selected for semiological purposes, to describe the effects of laboratory reengineering, and to provide a basis for comparative and connotative analysis of laboratory designs and instruments (as explained in Section 5.3.3.1).

Figure 6.4a. Teaching laboratory.

This analysis of a teaching laboratory begins with a structured, snapshot view, but as the analysis proceeds, fundamental structuralist tenets are demonstrated; namely that the teaching laboratory’s spatial organisation has more meaning when it is compared with industry laboratories, and also structure and evolution are intertwined (Balzer, & Moulines, 1996; Gibson, 1984; Piaget, 1971; Saussure, 1959). The laboratory can be described at the structured level by its analysis in the plane of expression (refer to Section 4.2.3.2). The laboratory is thus considered as an 187 ensemble of articulated components, co-existing in linear series by relations of combination (expression line). When the arrangement of objects in a laboratory is considered, spatial arrangement is a significant feature of invariance, from which similar but different laboratory arrangements are considered by virtue of association (e.g. large scale versus miniature) (expression side of virtual paradigmatic associations). Together the expression line and expression side constitute the plane of expression for all possible laboratory and instrument arrangements. Although such analysis is potentially infinite, the number of possible laboratory arrangements is quite limited. Two features are selected from analysis of the laboratory in the plane of expression, laboratory spatial arrangements of instruments, and the spatial arrangements of instrument components. Because laboratory instruments are constructed according to underlying physical principles of measurement, their analysis can be considered as logical and scientific (as demonstrated in Section 7.2.2). There is however no theoretical basis for a laboratory’s spatial arrangements, pragmatic managerial concerns such as availability of space, equipment, and budgets dictate the way laboratories are organised (AACB, 1999a, 2001). The analysis is unscientific and simply draws on structuralist organising principles. The laboratory can be treated as material substance with a specific form or spatial arrangement, and subjected to structural analysis by seeking out points of invariance in the combinations of its component elements. There are many material aspects of a laboratory that can be varied as starting points for this kind of analysis, as Barthes (1967/1990) demonstrates for the system of dress in the ensemble of clothing. There are points of invariance in the arrangements or ensembles of clothing, furniture, recipes and menus, and laboratories, the significance of which emerges through the semiological principle of the conceptual opposition or distinction (refer to Section 4.2.1). The differences between laboratories with respect to spatial arrangements can be used to signify organisational issues of laboratory practice, which come to light in comparative analysis, the basis of semiology and the structuralist method (Barthes, 1964/1973, 1967/1990). Within industry laboratories for example, there are conceptual oppositions with respect to job descriptions, scientist versus technician; instruments, manual versus automated; and modes of testing, routine versus specialised and esoteric (AACB, 1999a, 2001). The teaching laboratory can be compared with industry laboratories in terms of conceptual oppositions such as manual versus automated, miniature versus large-scale, dedicated 188 versus flexible, and esoteric versus routine. The conceptual opposition, manual versus automated, for example, highlights distinctive and differentiating features of core industry laboratories and totally automated laboratories (TLA), which stand in stark contrast with the more manually operated teaching laboratory, which has more in common with miniaturised Point-of-care-testing laboratories (POCT) (AACB, 2001; Bais, & Atkins, 2001). These conceptual oppositions highlight similar but different, therefore significant, spatial design logics and agendas driving different laboratory organisations. Two aspects of the teaching laboratory are considered, the space for the preparation of biochemistry, clinical biochemistry and molecular biology experiments (Figure 6.4a), and the space used to house a wide variety of instruments used to acquire data from experiments (the layout is represented schematically as it existed in 1998, Figure 6.4b) (see also Figure 7.1).

Computer FES MASgas cylinders AAS Immunoassay MPLC MFS TDx 6m RA1000 Cobas Fara LPLC Flexigem IMx DT60

Densitometer DTE g counter Stratus DTSC Chloride ISE Blood Gas

GC HPLC 10m

Electrochemistry Spectrophotometry Chromatography

Dry slide chemistry Radioactivity Automation

Figure 6.4b. Instrument laboratory layout.

The latter space is of greatest interest in this section because it more closely approximates industry laboratories. An entire floor in the School of Life Sciences is annexed for biochemistry and molecular biology practical classes. The other medical science disciplines, haematology, microbiology, histology, cytology and immunology occupy other floors in the same building. In this way the medical 189 science training sector emulates many hospital pathology laboratories in Australia, but not the major private pathology laboratories which tend toward large open spaces in order to accommodate large scale automated instruments for the performance of large numbers of tests each day (Kent, 1999). The instrument laboratory (Figure 6.4b) does not function as an autonomous unit, its stands apart from the main teaching laboratory (Figure 6.4a), because complex analytical instruments must be housed in air-conditioned spaces. The instruments constituting the macro-objects used in clinical chemistry laboratory practice are representative of the major classes of systems of chemical analysis, namely spectrophotometry, electrochemistry, chromatography, and immunoassay (refer to Section 7.2.1). Although the instruments are arranged (expression line) primarily for convenience due to limited space, there is an apparent functional logic or “syntax” in their spatial arrangements. As the arrangement reveals, spectrophotometric instruments are clustered together, as are the chromatographic and electrochemistry instruments. This spatial arrangement thus signifies the functional logic of instruments according to underlying physical principles, and the laboratory can be designated as a Mode 1 disciplinary space. By contrast, the spatial arrangements of industry laboratories described in Section 6.3.2 are geared for high test throughput and signify pragmatic managerial concerns of work flow and efficiency. Industry laboratories can thus be designated, according to their spatial arrangements, as tending towards Mode 2 transdisciplinary spaces (refer to Section 3.2). Other macro-objects required for biochemistry practical classes such as centrifuges (bench top, high-speed, and super-speed), are not represented in this schematic layout because they are housed elsewhere on the same floor where more space is available. For the sake of simplicity only macro objects are considered, but there are also accessory micro-objects used for sample and reagent preparations including auto-pipettors, micro-centrifuges, glassware, plastic test tubes, heating blocks, thermometers, barometers, and instrument manuals, flow charts and diagrams. It is important to note however, that the micro-objects of the laboratory signify the requirement for more refined technical skills, precision and accuracy of experimental results, which is difficult to achieve by manual pipetting of very small reagent and sample volumes (e.g. sample volumes as low as 5µL, where 1 L [litre] = 1000 mL [millilitre] and 1 mL = 1,000 µL [microlitre]). Miniaturisation of methods 190 is a feature all laboratories have in common, whether totally automated macro-scale laboratories or micro-scale POCT laboratories (AACB, 2001; Isaacs, 1999; Price, & Hicks, 1999; Wilding, 1998). Micro-objects also signify that developments in medical science and technology continually produce new tests and new techniques as old tests become routine and automated or obsolete (AACB, 1999a). The new tests, sometimes referred to as “esoteric” (infrequently ordered and manual or semi- automated), represent a significant aspect of a laboratory's output, not cost-effective, but part of the service (Bais, & Atkins, 2001). High precision technical work, essential for the attainment of valid test results, is difficult to achieve manually, and this has been a major factor in the general push to automation (AACB, 1999c; Rosenfeld, 1999). Miniaturisation and esoteric testing signify that the traditional craft aspects of medical laboratory science, geared for manual as well as intellectual work, are not completely obsolete. Even in highly automated laboratories there is sometimes a requirement for medical scientists to perform traditional techniques, manual methods, instrument calibration, data reduction, curve fitting and calculations, in troubleshooting, and in evaluation, optimisation and revision of methods (Martin, 1999). More meaning is derived from the spatial structure of the instrument laboratory if its evolution is also considered. Technological progress is impacting on the teaching laboratory, gradually transforming it into one that more closely emulates industry laboratories. This point is illustrated in Figure 6.4b by the mixture of manual and automated instruments. Outmoded manually operated so-called “dedicated” instruments, those committed to a particular function, are replaced in the laboratory in three ways. Firstly, single instruments with improved designs replace worn out instruments. Secondly, multipurpose automated instruments incorporate the functions of different instruments on one platform. Thirdly, instruments are not bought but leased for the teaching times for which they are required. Laboratory culture thus emulates everyday consumer society in the way objects/instruments are “consumed” or passed into disuse. A few examples illustrate these changes as follows. Since the time of mapping the laboratory’s spatial arrangements in 1998 (Figure 6.4b), there have been many changes, not least the instrument laboratory has been moved to make way for an increased demand for molecular biology classes, but changes in instruments only are considered. The electrochemical chloride meter that functions by coulometric-amperometric titration has been relegated to the storeroom 191 owing to the availability of alternative electrochemical instruments performing multi- tests, including chloride, with improved performance, by ion-selective electrodes (ISE). The blood gas analyser represented is no longer a permanent fixture, but is leased for the duration of the teaching period, its predecessor, although still functional, has gone into disuse due to unavailability of parts and services. This reciprocal arrangement, entailing the purchase of consumables only, is commonly encountered in industry laboratories in order to improve quality and efficiency of analysis with new instruments, and to maintain financial viability (Isaacs, 1999). The Stratus immunoassay analyser is functional for as long as its remaining accessories and parts are available. The gas chromatography unit (GC) has been removed for occupational health and safety reasons. This is because GC testing has specific requirements due to the use of a very hot oven to make samples volatile and amenable to gas chromatographic separation, with the aid of inflammable hydrogen gas (Burtis, & Ashwood, 1999). Spectrophotometric, electrochemical, and immunoassay approaches to chemical analysis are conducted manually or by automation (RA1000, Cobas Fara, TDx, IMx) (Figure 6.4b). Automated instruments may be multi-channel (different analytes in one specimen tested for simultaneously on the one instrument, e.g. RA1000) and/or multi-platform or multi-functional, that is many different approaches to analysis, spectrophotometric, electrochemical and immunoassay, can be performed on one instrument (e.g. Cobas Fara performs spectrophotometric and electrochemical analysis). The conceptual opposition manual/automated is represented in the labelling of instruments. The teaching laboratory has traditionally housed instruments that are labelled according to their function, for example a spectrophotometer is labelled a “spectrophotometer” (e.g. Figure 7.3 but the name is not in view). Increasingly however, as in industry laboratories, instruments are labelled with corporate logos, such as Roche, Abbott, and Bayer Diagnostics (e.g. Figure 6.5b but names are obscured). The signs of the instruments’ actual functions are buried under layers of automated, robotic and computerised functions. The labelling of instruments in this manner evokes the conceptual opposition “black box/white box” coined by systems theorists (Weinberg, 1975), and is further discussed in the “connotations” of objects (Section 6.3.4), following the next section which describes the way industry laboratories are being reengineered.

192

6.3.2 Alternative industry laboratory spaces

Recapitulating on the previous section, the teaching instrument laboratory is a Mode 1 disciplinary space because it is used for teaching students about many types of laboratory instruments based on their different physical principles. The instrument laboratory roughly approximates in size and modes of chemical analysis and function, the esoteric testing corner of a core pathology laboratory in the private sector (top right, Figures 6.5a, 6.5b, & 6.5c), which is representative of most high throughput industry laboratories (see AACB, 1999a, 2001; Bais, & Atkins, 2001 for discussions of core laboratories but minus photographs).

Figure 6.5a. Core industry laboratory. Figure 6.5b. Automated instruments.

Figure 6.5a. Core Industry laboratory. Figure 6.5b. Automated Instruments.

By contrast with the teaching laboratory in which instruments are placed for convenience, the arrangement of instruments in industry laboratories, being automated, is highly significant. As for the teaching laboratory, space is at a premium but the utilisation of space in industry is tied to the maximisation of workflow and efficiency, for performance of the greatest number of tests in the least time at the smallest workstations with the fewest operators at the lowest cost (AACB, 1999a, 2001; Bais, & Atkins, 2001; Isaacs, 1999). Although industry workflow has no counterpart in the teaching laboratory, students are expected to work efficiently in limited time and space, and both contexts are united in their goals for reliability and validity, precision and accuracy or high quality results, and their commitments to occupational health and safety issues. 193

In large-scale industry laboratories, high throughput analysers tend to be clustered together where the bulk of the work can pass through quickly and efficiently (Figures 6.5a & 6.5b). Esoteric tests (infrequently performed and unusual tests) are performed on low or medium throughput analysers and sometimes manually in an area set apart from the main thoroughfare of the laboratory (AACB, 1999a, 2001; Bais, & Atkins, 2001) (top right, Figure 6.5c). The core laboratory as represented in Figures 6.5a and 6.5c illustrates the general trend towards open plan reengineering in laboratory designs (Lincoln, 1996).

6m Automation BIOCHEMISTRY Esoteric tests SPECIMEN PROCESSING High throughput 10m instrument clusters

Medium & low throughput analysers

IMMUNOLOGY HAEMATOLOGY

ENDOCRINOLOGY MOLECULAR DIAGNOSTICS Immunoassay

Figure 6.5c. Core industry laboratory layout.

The process of reengineering is ongoing and is associated with different trends, ranging from total laboratory automation (TLA) (Boyd, Felder, & Savory, 1996; Wilding, 1998) to modular designed core laboratories (AACB, 1999a, 2001; Bais, & Atkins, 2001; Wilding, 1998), to miniaturised POCT laboratories (AACB, 2001, 2002a; Price, & Hicks, 1999). In Japan and the United States of America (USA) the reengineering process has culminated in the macro-organisation of very large factory style laboratories in a process referred to as “total laboratory automation” (TLA) (Boyd et al., 1996) (Figure 6.6) (note that this laboratory design has been drawn from the literature, and no size specifications are given, however it is likely given the testing clusters represented, that its size would be roughly equivalent 194 to the entire floor plan represented in Figure 6.5c). Few large core laboratories can afford to operate along these lines due to the constraints of space and budgets (Wilding, 1998).

Chemistry Haematology identify cluster cluster Front end centrifuge automation aliquot Systems label control Specimen sort Immunology Coagulation processing imaging cluster studies storage transport

Molecular Diagnostics

Figure 6.6. Total laboratory automation (adapted from Boyd et al., 1996).

Most large-scale high throughput Australian laboratories have opted for flexible modular designed instruments that are readily adaptable to existing spaces (AACB, 1999a, 2001). The open plan laboratory represented in Figure 6.5a is typical of the design logic of many high throughput (Australian) laboratories, particularly in the private sector (AACB, 1999a, 2001). Hospital laboratories have tended to occupy multi-story buildings with long central corridors, not amenable to open plan reengineering, although they too are reengineering in newly designed laboratories and new buildings (Kent, 1999). Such building constraints give the public pathology sector at least the outward appearance of commitment to the disciplinary structures emulated in the university sector. The situation is however constantly changing, driven by technological advances and multi-platform instrument designs that are forcing consolidation of the disciplines into smaller integrated work stations (AACB, 1999a, 2001). The trend towards the consolidation of disciplines, particularly biochemistry, haematology, and immunology, is represented in the open plan private sector laboratory in Figure 6.5c (note that only the biochemistry section is captured in Figures 6.5a & 6.5b, but the instruments can perform haematology and immunology tests as well). There is no certainty about future developments in laboratory designs, and there may be fewer long-term prospects for TLA than there 195 are for miniaturised POCT (AACB, 1999a, 2001; Boyd et al., 1996; Price, & Hicks, 1999; Wilding, 1998). There will be further convergence of the disciplines with miniaturisation and POCT (Price, & Hicks, 1999), advances in molecular diagnostics (AACB, 1999c), and automated specimen processing, also called “front-end automation” (AACB, 1999a, 2001) (right side, Figure 6.6). Miniaturised POCT, as discussed in Section 2.3, is well established for home glucose monitoring of Diabetes Mellitus (Figure 6.7a), in intensive care assessment of acid base balance in critically ill patients (Blood pH and blood gases, pO2 and pCO2), in blood lactate monitoring for athletics training regimes, and in cholesterol screening for coronary heart disease (CHD) prevention (Price, & Hicks, 1999). Figure 6.7b provides a schematic representation of a hand held device of the type used to test a range of commonly requested analytes in the emergency or intensive care situation (Wilding, & Ciaverelli, 1999).

Reference Na K Cl Reference Na 0.000 CO K 2 Cl CO 5.20 Ca 2 Ca Glucose Glucose NH4 NH Lactate 4 pH pO Lactate 2 pCO ISE 2 pH

pO2 pCO2 SAMPLE INPUT

Figure 6.7a. Glucometer. Figure 6.7b. POCT by ISE.

This hand held device is dedicated to testing by Ion Selective Electrodes (ISE), however miniaturised testing is being broadened with microchip technology developments to include immunoassay, electrophoresis, chromatography and various DNA technologies (Kricka, & Wilding, 1996; St John, 2002; Wilding, 1998; Wilding, & Ciaverelli, 1999). POCT in its earlier manifestations was synonymous with manual testing. Next, remote automated laboratories were envisaged as small, portable, and accessible to operators through touch screen interfaces, not unlike bank teller machines (Boyd et al., 1996). Now POCT is increasingly automated and computer networked with central laboratories to facilitate information transfer and 196 quality control (St John, 2001). These advances are particularly evident in home glucose monitoring and coagulation monitoring of cardiac patients (AACB, 2002a; St John, & Ward, 2002). There may be few prospects for medical scientists in the realm of POCT, as pundits place it in the hands of general medical practitioners (St John, 2002). Future directions in laboratory organisations are subject to debate, and the province of laboratory managers and the various pathology professions. The work prospects of medical scientists are similarly subject to debate (AACB, 1999a). Peter Wilding (1998), director of Pathology Services in Pennsylvania and designer of TLA and microfabrications, acknowledges that the future for the scientific workforce is uncertain. The consolidation of disciplines is leading to an increased requirement for multi-tasking and multi-skilling (AACB, 1999a; Med-Tec International, 1996). Wilding (1998) suggests that more skills and more cross training are needed, especially in the production of microchip technologies for the expanding fields of molecular diagnostics and POCT. POCT will however most likely be conducted by medical practitioners (AACB, 2002a). In this discussion about laboratory spatial arrangements, the trail has been followed over the last five years, in clinical biochemistry journals and newsletters, particularly those associated with the AACB. There is general agreement that automation, robotics and informatics are leading to fewer job prospects and an uncertain future for medical scientists. There is less emphasis placed however, on the additional impact of laboratory information systems, including management systems such as the ISO 9000 series (International Organisation for Standardisation) (ISO, n. d.) and Expert Systems (Sikaris, 2001), on scientists’ roles and work prospects. There is even less discussion of the possibilities in EBLM, which Morris (2000) claims is an omission from the curriculum. The future for medical scientists is open to speculation, and the question remains as to whether medical scientists are being de-skilled and displaced, as discussed in Section 2.4. Speculation about the work situation is continued in the connotative analysis of laboratory instruments in Section 6.3.4, after the following implications are drawn from spatial analysis.

6.3.3 Implications of spatial analysis

Spatial analysis of objects in the plane of expression is guided by the structuralist principle in semiology, that things can be described by their ordering in 197 the expression line, or their linear relations of juxtaposition, and by their comparisons with associated points that are similar but different, in the expression side of virtual, conceptual oppositions. Spatial analysis was applied loosely in the previous section, for the purpose of describing the teaching laboratory and for comparing it with industry laboratories, with the emphasis being placed on technological progress and organisational issues. To take this aspect of the analysis any further would require more detailed treatment of industry laboratories, and an exploration of managerial issues. Laboratory managers are principally concerned with the economic viability of the laboratory and service, and the organisation of space is geared towards cost effectiveness, performance, and efficiency, optimised through laboratory designs and workflow patterns (AACB, 1998b, 1999a, 2001; Weiss, & Ash, 1999). The purpose of semiology is to use the denotative system of objective descriptions as points of departure for connotative analysis (refer to Section 4.2.3.3). There are values that underpin laboratory organisations and culture, which are “spoken” in the “rhetoric” of laboratory and instrument designs. The analysis of laboratory spatial arrangements highlights the oppositional trend, multi-skilling/de- skilling. On the one hand, there is a convergence of disciplines in the reengineered laboratory and a need for multi-skilling (AACB, 1999a, Med Tech International, 1996). On the other hand, there is the potential for displacement and perhaps also de- skilling, due to automation, robotics and informatics taking over traditional roles (AACB, 1999a). By “operating” at the connotative level, the semiologist aims to tease from the denoted system, issues of social significance and insights into the experience of consumers. In the next section, the issues of de-skilling and displacement are connoted in the black-box forms of laboratory instruments. Further insights are sought into the situation, by linking the transformations in laboratory organisations and technological advancements to government fiscal policy, the cost of health care, shrinking health budgets and reductions in Medicare reimbursement schedules which influence the way pathology testing is conducted (AACB, 2000b; Farrance, 2000). Before conducting the connotative analysis it is noted, following Barthes (1964/1973), that semiological analysis encroaches on the domain of many specialist disciplines such as economics, psychology, and sociology; but the purpose of doing semiology is to cross disciplinary boundaries and seek out matters of social 198 significance arising from systems of signification, which must then be followed up by more comprehensive and specialised analysis (p. 96).

6.3.4 The “connotations” of laboratory instruments

There are no restrictions on the “connotations” of objects, and so there are many connotations implied in laboratory instruments. As Barthes (1967/1990) explains, connotative analysis requires an “operation” performed by an analyst imposing “nomenclature on latent signifieds” in the endless connotations of the rhetorical system (p. 292). Such analysis is therefore unique, an operation performed on the denoted system according to criteria the analyst deems pertinent (Figure 4.18, Levels III & IV). Several issues guide this otherwise unstructured analysis of the “connotations” of laboratory objects. Firstly, there is a requirement for professional courses to consider the changes occurring in workplaces, universities and modes of knowledge production, from disciplinary Mode 1 knowledge systems to transdisciplinary, cooperative Mode 2 knowledge systems (Gibbons et al., 1994) (as discussed in Section 3.2). Secondly, as Schön suggests (1983, 1987), a technically rational model of professional education is incomplete, and understanding is needed of the nature of work, knowledge work and symbolic analysis, in the new work environment of reengineered laboratories (Gee, Hull, & Lankshear, 1996; Gerber, & Lankshear, 2000). Thirdly, as analysts of work suggest, in computerised technical work environments there has been a shift in skills requirement from “craft” work, requiring both manual and mental labour, to abstract intellectual work, knowledge work and symbolic analysis (Aronowitz, & DiFazio, 1994; Barley, & Orr, 1997; Drucker, 1993; Gerber, & Lankshear, 2000; Reich, 1992). Finally, there are several broad aspects of competence to consider, along a continuum from “d” operational, to “D” cultural (expert in a Discourse), and critical (critical of a Discourse), for knowledge work and symbolic analysis in computerised technical environments (Lankshear, 2000) (see Section 3.5.1). It is necessary to consider what skills are needed for optimal laboratory work performance, and what new skills are needed for critical reflection in EBLM and other Health Technology Assessments (HTA). Connotative analysis is guided by Hjelmslev (1943/1961, 1970) who refers to the degrees of language, the meta-languages through which a denoted language can “speak” the historical, geographical, national, institutional, and other aspects of a 199 culture (Section 4.2.2). In connotation, things are implied, it is not what is said, but the way it is said, being rhetoric, that matters (form of expression); and values, being ideology, underlie that which is expressed or spoken (form of the content). In connotation, semiology aims to interconnect signs, structures, codes, and events with the larger social system (Barthes, 1967/1990, pp. 247, 291). A socio-semiotic approach tackles socio-cultural and ideological issues by placing emphasis on the articulation between codified ideology (form of the content) and its material manifestations (form and substance of the expression) (Gottdiener, 1995). The ideology of a social system materialises in the rhetoric of everyday spaces, buildings, architectural designs and theme parks, and socio-semiotic analysis gives insights into the workings of culture, and the power differentials impacting on consumer experience (refer to Section 4.2.4). Laboratory environments can be treated in a similar manner, so that the material forms of the laboratory will articulate with interventionist medical science ideology and the technical Discourse of instrument and computer technologies. The rhetoric of laboratory practice is connoted in the forms or morphologies of laboratory instruments, which in turn express the values or ideology of laboratory practice. In summary, socio-semiotics, semiology (and other forms of cultural analysis such as Foucaultian archaeology [1969/1972]), aim to explore the articulation between values, ideology, power and material forms. The issue of subjectivity is raised in the process, and the way individuals are constrained (subjugated) to the codes of culture, and the purposes of Discourses. The purpose of seeking socio-ideological insights into laboratory culture is to draw distinctions between the levels of competence, “d” operational competence to perform routine work effectively; “D” cultural competence for critical reflection within clinical chemistry Discourse, in the evaluation, optimisation and revision of methods; and “D” competence for critical reflection on medical science Discourse, in the evaluation of laboratory tests in EBLM. There are two components to the connotative analysis of laboratory instruments. The first component is concerned with “D” competence and optimal work performance based around analytical validity as set out in Section 6.2.2 (Figures 6.2 & 6.3, Levels 2-6). Using Schwab’s terminology (1964b), the relatively stable “short term syntax” of clinical chemistry consists of a body of imposed conceptions with specific pathways to verification controlled by reliability and validity criteria. A technically rational approach to medical science education will 200 stay within the boundaries of this imposed knowledge structure, and will fail to encourage students to consider alternative perspectives (Schön, 1983, 1987). Technical rationality in clinical chemistry is inscribed in material forms or instruments, which is why Section 6.3.4.1 is entitled “the rhetoric of laboratory instrument design”. This section will reveal that the technically rational model of education is problematic for medical scientists if it colonises them to remain within the Discourse (Gee, 1996). An antidote to Discourse colonisation, de-skilling and displacement, is proposed in the second component of connotative analysis (Section 6.3.4.2), entitled “The ideology of laboratory testing”. “D” competence, critical of the Discourse, is needed in order to seek out inconsistencies, inadequacies and contradictions in the “long term syntax of fluid inquiry” of clinical chemistry laboratory testing (following Schwab, 1964a, p. 39). The knowledge worker in clinical chemistry Mode 2 Discourse directs criticism towards the evaluation of laboratory tests as well as methods in EBLM, and thus completes the laboratory test loop (Figures 6.2 & 6.3, Level 1).

6.3.4.1 The “rhetoric” of laboratory instrument design

This section is concerned not with instrument functions but with what they communicate to users, what is implied in their designs that constrain work to a technically rational experience. There will be any number of ways to consider the “rhetoric” of laboratory instruments, for what they communicate to users. Barthes (1967/1990) refers to the shift from the denoted, descriptive system to the connoted rhetorical system as a shift from “function to spectacle”, requiring what he calls a “‘poetics’ of the machine” (p. 235). There is a poetics of laboratory instruments, particularly those that are automated and operated by robots. The artistry and technological wizardry that goes into the construction of laboratory instruments makes them suitable subject matter for aesthetic analysis and art museums. This section aims however, to come closer to a meaning for knowledge work and symbolic analysis in highly automated computerised laboratories. This requires putting technological wizardry aside, and observing the way the intellectual functions of chemical analysis have become buried inside black box automated instruments. Whereas laboratory instruments are given more detailed treatment in terms of their functions and principles of analysis in Chapter 7, in this section they are considered 201 as “black-box” forms, borrowing the term from cybernetics and systems theory (Weinberg, 1975). A “black box” is a conceptual device that permits access to inputs and outputs but not to the contents or processes in between. The term “white box” is a three- dimensional reconstruction or model that represents the inner workings of a device or problem (Bunn, 1981, p. 64). A white box is an abstract symbolic representation in which only the aspects of interest are given prominence. As Bunn (1981) explains for a model of the central nervous system, the nerves become prominent and other aspects are pared away, so that muscles and skeleton are represented only in as much as they maintain important links with nerves (pp. 64-65). Latour (1987) provides an example in the automatic Kodak camera, explained as a black box assembly in which “a large number of elements are made to act as one”, made of bits and pieces but cannot be “dismantled, renegotiated or re-appropriated” (p. 131). Abstract white box models are important in teaching and learning situations dominated by black box instruments that permit access to inputs and outputs, but obscure the functional logic or principle of operation. The black box stands as a metaphor for the technical rationality of laboratory experience, in which functions are reduced to inputs (reagents and specimens) and outputs (results), and functional mechanisms, physical, chemical and mathematical principles of operation are obscured. Industry laboratories in the 1970s and 1980s introduced technologically advanced instruments incorporating robotics and automation (Rosenfeld, 1999). Automation at that stage was frequently accompanied by rhetoric such as “walkaway capability”, and instrument designs became increasingly closed off from operator interventions to such an extent that the slogan might just as well have been “hands- off/minds-off”. Walkaway capability was never a reality in the laboratory, there must be a “minder” for every automated machine (AACB, 1999a). This applies also to more advanced instrumentation incorporating diagnostic Expert Systems to control operations, and diagnose and troubleshoot errors (Sikaris, 2001). The clinical chemist is a symbolic analyst who is constantly vigilant and alert to the possibilities of error and the need for interpretation, in spite of the interpretations of Expert Systems. Unreflective laboratory practice is simply a superficial manipulation of signifiers (expressions), button pushing according to clearly prescribed instructions. The laboratory for the instrument minder is a field of “free floating signifiers”, signifier and signified, expression and content, instrument and meaning in physical theory are 202 cast asunder. A more mindful approach to laboratory practice is demonstrated in Section 7.3, in terms of semiotic logic, the recognition of signs of instrument malfunctions and method errors, as symptoms and clues represented in instrument readout windows and graphic inscriptions. Black-box instruments associated with automation are a necessity in the pathology industry, to minimise interferences such as stray light, to minimise the error associated with human interventions, and for maximum performance and efficiency (Rosenfeld, 1999). It is important to note however that the black-box metaphor is not just applicable to automated instruments. With automation, more layers are added that obscure the primary functions of instruments, but the technically rational mode of operating in laboratories has been prominent ever since “first order” instruments, manually operated instruments, made laboratory measurement possible (Meloan, 1968a, 1968b; Rosenfeld, 1999). At a time when manually operated instruments were prominent in laboratories, chemistry professor Clifton Meloan (1968a, 1968b), noting that many technologists failed to grasp the significance of different instrument functions, prepared courses in instrumental analysis to help them integrate theory and practice. There is some chance of such integration with “first order” instruments which are labelled according to their functions, for example, a spectrophotometer, based on interactions between matter (atoms and molecules) and EMR, is labelled as such (Figure 7.5). The name signifies its function, to identify and measure the concentration of elements and compounds based on their interactions with EMR (as explained in Section 7.2.2). “Second order” automated instruments further obscure their functions by being labelled with corporate logos such as Bayer, Abbott and Roche (Figure 6.5b). Technologies in clinical chemistry laboratories have gone far beyond assisting scientists to do their work, which was their function in the 1970s and 1980s (Rosenfeld, 1999). Informatics, computerised diagnostic Expert Systems, and auditing systems such as the ISO9000 series now perform many of the interpretive functions formerly assigned to medical scientists (Sikaris, 2001). These functions include troubleshooting operations, detection, diagnosis and correction of systems engineering faults, quality monitoring and clinical interpretation of results. ISO9000 and other ISO standards are computerised quality management systems produced by the International Organisation for Standardisation (ISO) to promote standardisation of related activities and facilitation of the international exchange of goods and 203 services (ISO, n. d.). The ISO standards now play a major role in laboratory management, by auditing activities such as tracking sources of error and monitoring performance (AACB, 1999b; Westgard, & Klee, 1999). Thus, information technology (IT), information management systems and laboratory management skills are now integral aspects of medical scientists’ roles (Sikaris, 2000, 2001). The technological literacy required for troubleshooting instruments in the 1970s and 1980s, must be distinguished from the information literacy needed for management of the vast amounts of information generated in highly automated laboratory environments (Boyd et al., 1996) (see also, Bruce, & Candy, 2000; Lankshear, 2000; Tinkler, Lepani, & Mitchell, 1996). There are limited avenues of employment in laboratory management however, and despite the trend towards multi-skilling (operating effectively in all the disciplinary areas) and multi-tasking (performing several tasks at once) (AACB, 1999a; Med Tech International, 1996), automation, robotics and informatics are leading to fewer prospects for job promotion (AACB, 1999a). The traditional roles of base level medical scientists are continually being eroded and there is a perception in the industry that the situation will worsen (Martin, 1999). There have always been very few openings for senior scientists because the turnover at this level is very low, so that opportunities for career advancement are quite restricted. This bleak picture of the future of work for medical scientists is described by analysts of work as a general condition of computerisation and automation (Aronowitz, & DiFazio, 1994) (refer to Section 2.4). However, developers of TLA speculate that the technological advances will free technologists from repetitious chores to manage the information generated in laboratory testing. They expect that job satisfaction will be enhanced by the broader crossdisciplinary roles required for the assessment and implementation of new technologies, clinical consultancy, laboratory resources management, information systems, POCT, the improvement of quality, and the promotion and utilisation of laboratory testing (Boyd et al., 1996; Wilding, 1998). Optimistic analysts of work argue against de-skilling as an outcome of computerisation in professional technical work environments (see Aronowitz, & DiFazio, 1994, pp. 81-103). The optimists envisage “artisanal craft communities” using computer technologies, operating according to the “flexible specialisation” management strategy of small batch “just in time” production runs operated by skilled workers in decentralised shops (p. 97). The counterpart of this idea in the 204 pathology industry is POCT. However, as explained in Section 2.3, POCT will more likely be conducted by non medical scientists, particularly general medical practitioners (AACB, 2002a). It is likely that all professional computerised workplaces will require more skills, but there will be fewer highly skilled jobs, the problem is “displacement” not de-skilling (Aronowitz, & DiFazio, 1994, p. 98). Two broad issues of educational significance emerge from this brief exploration into the “rhetoric” of instrument design, which expand on the discussion in Section 2.4. On the one hand, in order to counter the potential for de-skilling, more understanding is needed about skilled work, knowledge work and symbolic analysis. On the other hand, in order to counter the potential for displacement, it is crucial to focus on generic skills such as critical thinking and information management, to open up avenues of employment within or outside the medical science field (Bruce, & Candy, 2000; Candy, 2000; Lankshear, 2000). There is a very limited range of studies addressing symbolic analysis specifically to the medical sciences. One extensive North American study explores the nature of technical work, including medical technology (the American counterpart of Australian medical science) (Barley, & Orr, 1997). This American study notes the blurring of boundaries between mental and manual work, placing medical laboratory work in a category caught between “craft and profession” (p. 41). This is because the work entails manipulation of physical and symbolic worlds in instrument use, and instrument data outputs and their graphical and mathematical inscriptions (p. 40). Three core aspects of medical laboratory technology aimed at minimising error identified in the American study are equally applicable in the Australian context, to the work of medical laboratory scientists (Barley, & Orr, 1997). Firstly, “interpretative skills” are needed for understanding test results in the contexts of technical and formal knowledge, “tacit and working knowledge”, and knowledge of a patient’s individual history in order to make sense of anomalous findings (p. 199). Secondly, “troubleshooting machine malfunctions” requires knowledge of machines in order to diagnose functional errors (p. 202). Thirdly, “improvisation and artistry” is needed for the development of “on-the-spot techniques for controlling unexpected variations in test materials” (p. 203). The modification of methods to maximise performance, their adjustment to local conditions and circumstances, or optimisation, can also be classified under this 205 heading. These are the levels of skills required for operational and cultural competence in the medical science disciplines. Interpretive skills, troubleshooting and innovation cannot be accounted for simply in terms of information or knowledge. It is necessary to integrate academic and experiential knowledge (Laurillard, 2002). Analysts of work (e.g. Aronowitz, & DiFazio, 1994) make reference to the elusive notion “tacit working knowledge” or “tacit knowing” for which the philosopher Michael Polanyi (1969) is particularly noted (refer to Section 3.5.2). Polanyi claims that there is a “close analogy between the elucidation of a comprehensive object and the mastering of a skill” (p. 125). Tacit knowing has a structure, and is based on the “tacit integration” of objects and perceptions in the environment towards the discovery of ideas (p. 139), roughly equivalent to the Peircean sign triad (p. 181) (refer to Section 4.3.2). Tacit knowledge is generally recognised as that which is unwritten, residing with those who possess it. Instruction manuals although incorporating troubleshooting sections fail to capture the idiosyncrasies of technical equipment that are grasped through trial and error in practice. This kind of knowledge accumulates through on the job training and experience, and distinguishes academic knowledge characterised as abstract and symbolic from technical work such as engineering (Aronowitz, & DiFazio, 1994; Barley, & Orr, 1997; Gibbons et al., 1994). It is for this reason that Barley and Orr (1997) claim that technical laboratory work and engineering “violate the cultural segregation of the material and the symbolic”, or between manual and mental labour (p. 47). Laboratory technicians and technologists work at this intersection by manipulating data and symbols derived from their use of sophisticated techniques and technologies. Aspects of the material world, physical phenomena, are thus transformed into data and other symbolic representations or inscriptions used to enhance diagnosis (p. 47). Computerised knowledge work in laboratories is “technized” because it requires “an ability to intervene in the world of objects through symbols” (p. 10), and hence its characterisation as symbolic analysis. There are two problems with the appraisal by Barley and Orr (1997) of laboratory work with respect to the pathology industry in Australia. Firstly, it is an overly romantic view of laboratory technicians’ work (although not of engineers’ work) in the light of laboratory regulations such as CLIA88 (Clinical Laboratory Improvements Amendment) that require many of the activities described by Barley and Orr to be performed by those with higher scientific credentials, that is scientists 206

(AACB, 2000b; Ehrmeyer, 2000). Secondly, automation, robotics and Expert Systems perform many of the “tacit” components of laboratory work, troubleshooting, diagnostics, data and results validations, and increasingly clinical interpretations (Sikaris, 2001). Aronowitz and DiFazio (1994) describe a displacement of both technical and scientific labour by informatics. They argue that the “digitalization of machinery” which is exemplified by computers, “signifies the final triumph of science and technology over craft, abstract over concrete labour, the mind over the body” and the dominance of intellectual over manual labour (p. 32). Because “number is the language of science” and “the body is the language of craft” (p. 32), the “concept of ‘skill’ associated with the craft era has given way to knowledge” (p. 88). “High production regimes” are no longer skills dependent in the traditional manual and technological sense, but knowledge dependent (p. 102). However the way tacit knowledge is often used to explain craft skill negates its underlying element, “the unity of body and brain” (p. 88). Tacit knowing understood as an integration of mental and manual labour is closer to that which Peirce (1931- 58) and Polanyi (1969) intended. Representations, the object world, and the world of the mind are inextricably linked. Due to computerisation, “intellectual skill” is the “wave of the future” and work is “intellectual, abstract, theoretical, and scientific” (Aronowitz, & DiFazio, 1994, p. 102). The knowledge worker deals with abstract symbolic representations and the work is symbolic analysis (Reich, 1992). The tacit knowledge needed for the craft activities of medical scientists in their dealings with instruments must be transferred to their dealings with symbols in the inscriptions produced by computers and instruments. The knowledge worker or symbolic analyst in the clinical chemistry laboratory will supervise computers in order to ensure that Expert Systems are making the right diagnoses of instrument errors, results validations, interpretations, and decisions (Sikaris, 2001). As Gillies (1996) asserts, developments in computing will not render human thinking superfluous but will rather stimulate human thinking to higher achievement (p. 154). However, unless knowledge work and symbolic analysis are consciously targeted, and the boundaries of work are ever expanded, the medical scientific workforce will ultimately be supplanted by Artificial Intelligence (AI). Given the level of computerisation in the clinical laboratory, competence will have to be considered in terms of intellectual work, knowledge work and symbolic analysis. If knowledge work resides inside peoples’ heads, then ways must be found 207 to make it more visible (Aronowitz, & DiFazio, 1994; McGee, 2002). The visibility of knowledge work and symbolic analysis in the medical sciences can be improved using semiotic logic as explained in Section 4.3, and demonstrated in Chapters 7 and 8. It remains in this chapter to explore the problem of displacement of medical scientists by expanding the notion of “D” competence to social criticism in EBLM. This is based on the assumption that social criticism will add value in EBLM, and also expand the career options and horizons of medical scientists.

6.3.4.2 The ideology of laboratory testing

This section considers the social accountability requirements of EBLM, and what additional skills are needed by medical scientists engaged in assessing laboratory tests for their clinical relevance, economic viability and appropriateness to the communities being served by the pathology industry (Muir-Gray, 1997; Price, 2001). The exploration in this section is a response to Gibbons’ (1999) challenge to universities to reconsider their courses, in terms of the requirements of transdisciplinary curricula, and the core skills needed for operating effectively in Mode 2 knowledge systems (p. 24). A Mode 2 transdisciplinary science curriculum will demonstrate the long-term syntax of fluid enquiry of a discipline, as well as its short-term syntax of stable inquiry (Schwab, 1964b, p. 39). It will make it clear that a discipline is not a stable body of facts but an imposed body of conceptions, unstable, full of contradictions, constantly under revision, and that there are many ways of formulating problems from different disciplinary perspectives. Scientific disciplines will be considered for their theoretical content, and also their value orientations, what motivates their investigations. For this purpose this section makes a crossdisciplinary exploration of the forms of social criticism that are applicable in EBLM. EBLM, which is in the early stages, has its origins in the established practice of Evidence-Based Medicine (EBM) (Muir-Gray, 1997). EBM, which aims to improve the quality of medical practice and enhance accountability, is defined as “the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients” (Sackett, Richardson, Rosenberg, & Haynes, 1997, p. 2). Its primary function is to enable doctors and patients to make decisions about healthcare on the basis of best available scientific evidence. Healthcare decisions are based on a wide range of activities conducted under the 208

EBM umbrella. To complement standard medical research practices such as “gold standard” random controlled trials (RCT), cross sectional and longitudinal cohort studies and case studies, EBM conducts systematic reviews of existing research into medical interventions, and health outcomes research of their effectiveness, safety and quality (Muir-Gray, 1997, p. xi). There is a need to eliminate bias in the assessment of treatments and to assess whether treatments are more harmful than beneficial (p. 31). These EBM activities are being extended to other HTA, including pathology testing (EBLM), drug interventions and radiological imaging. HTAs are used to assess technical and diagnostic performance and the clinical and operational impacts of medical technologies, to support decisions on their acceptance or rejection (Price, & Hicks, 1999, p. 13). A test must be cost effective, efficient, and appropriate as well as technically proficient and clinically relevant, and it must do more good than harm to the patient, the community, and the economy (Muir-Gray, 1997, pp. 17-19). EBM is not without its critics. The assessment of medical interventions can be conducted at a number of levels, but it may be limited to the assessment of gold standard RCTs, cohort and case studies, based on a commitment to the Western medical model. Others argue that there may be a hidden agenda in EBM, as the following mock Socratic dialogue implies:

Enthusiasticus was expounding the importance and potential of the EBM movement, arguing, as many would agree, that healthcare professionals could no longer afford to ignore questions about the effectiveness of what they do. Socrates, whilst agreeing with the principle, argued that medicine had always paid attention to effectiveness, and warned Enthusiasticus that the movement was really a vehicle for tightening management control over medicine and how resources are spent. (Grahame-Smith, 1995, p. 1126)

Alternatively, EBM may be more broadly conceived in terms of wider social issues as is the case in Evidence-Based Health (EBH) (Popay, & Williams, 1998). EBH also termed knowledge-based health (KBH) shifts the primary health care focus toward health care in general, more broadly defined in terms of policy, health budgets, change management, and justice, access and equity in the healthcare system (Stevens, & Milne, 1998). EBH thus seeks to integrate values and resources with evidence in health policy decision-making (Muir-Gray, 1997, p. 1). In EBH perspectives, health care decisions are not based solely on “research-based evidence about the consequences of treatment” but are “augmented by the intelligent use of 209 wider information, for example finance, patient flows and healthcare politics” (Stevens, & Milne, 1998). EBH attempts to reconcile the differences between interventionist and social and preventive approaches to medicine. This opposition is represented in myth, between Panacea and Hygieia the daughters of the Greek God of healing Asclepius, symbolising the attitudes present in modern medicine. Drugs and cure all remedies are offered as a panacea, as opposed to hygiene, good living and moderation being the key to good health (Dubos, 1980, p. 321). Thus EBH attempts to come to terms with the social criticisms of Western medicine as is conducted by medical sociology (Petersen, & Bunton, 1997). EBH and medical sociology provide the additional perspectives needed for evaluating laboratory tests for socially accountable laboratory medicine. There is no single medical sociology perspective, the literature covers such areas as instrumental rationality and bureaucracy, medicalisation critique, governmentality and risk, and draws insights from varied sources in philosophy, sociology and political economy, including those of Karl Marx, Max Weber, Theodor Adorno and Michel Foucault (see O’Farrell, 1997, & Petersen, & Bunton, 1997, for a wide range of perspectives). The medical sociology literature also focuses on the “bureaucratic mentality” that makes the interventionist, instrumental, approach to medicine possible (Turner, 1997, p. xvii). Many of these ideas run counter to or at least moderate the views from the “orthodox medicalisation critique” that dominated debates in the 1960s and 1970s. Criticisms were based in the perspectives of liberal humanism that championed the importance of individual freedom, human rights and social change (Lupton, 1997, p. 95). Ivan Illich (1976) was vociferous in denouncing the mainstream medical establishment that, he argued, had become “a major threat to health” (p. 11). “Clinical iatrogenesis” is medically induced injury, comprising “all clinical conditions for which remedies, physicians, or hospitals are the pathogens or ‘sickening’ agents” (p. 36). “Social iatrogenesis” is the outcome of the medical sponsorship of sickness, the reinforcement of “a morbid society” encouraging people to consume “curative, preventive, industrial and environmental medicine” (p. 42). To overmedicalize is to expropriate health, to destroy peoples’ ability to deal with their weaknesses in an autonomous way (p. 42). It becomes “cultural iatrogenesis” and medical colonisation when it paralyses individuals and prevents them from making “healthy responses to suffering, impairment and death” (p. 42). Proponents of the medicalization critique aim to 210 promote a wider appreciation of the recuperative powers of the body, which in turn they expect will curtail the public demand for useless, expensive and harmful treatments (Moynihan, 1998, p. 10). If the Foucaultian concept of governmentality is applied, medical practice in its various guises is given recognition for its contribution to the knowledge and practices by which we have come to understand and experience our bodies (Lupton, 1997, pp. 98-99). From this perspective, it is impossible to alter the balance of power that is skewed towards the medical profession rather than patients. Medical power is a resource by which diseases and illnesses are identified and dealt with, and patients are willing participants in this model (p. 99). Medicine as institution is surreptitiously “coercive, normative and also voluntary”, exercising a kind of “hegemonic authority” given that it finds acceptance as “legitimate and normative at the everyday level” (Turner, 1997, p. xiv). A brief excursion into the cholesterol story illustrates the multi-layered complexity involved in evaluating laboratory tests in EBLM, incorporating socially critical perspectives of Western medicine. Cholesterol is a clinical chemistry test that has been in use for at least half a century to assist clinicians in the diagnosis, prognosis, prevention and treatment of CHD. The cholesterol story is open to many interpretations. The laboratory test is but one of a battery of possible health technology interventions in the war against CHD, including the cholesterol lowering “Statins”, diagnostic imaging (Computer-assisted tomography [CAT] and magnetic resonance imaging [MRI]) and surgical intervention by angioplasty or coronary bypass of blocked arteries (Bais, & Burnett, 1999). A systematic review of the literature into cholesterol and CHD would illustrate Schwab’s concept of the long term fluid nature of scientific inquiry (1964b). It would reveal a shift in the ground of the cholesterol story, simplified here in terms of two broad hypotheses, the lipid hypothesis and the oxidative damage hypothesis. The lipid hypothesis is based on the belief that dietary fat, particularly saturated fat, plays a major role in the promotion of CHD (Keys, 1980). The oxidative damage hypothesis proposes that free radical damage to lipids is the problem, not lipids per se (Duthie, Wahle, & James, 1989). The latter hypothesis arose from contradictions in the lipid hypothesis, as is illustrated by the “French paradox” (Renaud, & de Lorgeril, 1992). The French population, although consuming a relatively high quantity of fat, failed to meet the expectations for CHD incidence and prevalence given by epidemiological evidence. 211

Diet and lifestyle differences were used to explain these epidemiological discrepancies at the phenotypic level (blood fat profiles), in particular red wine intake due to its anti-oxidant properties. A systematic review, as would be conducted in EBLM, is required to assess the current status of these hypotheses, whether they are confirmed, negated or integrated. The ability to characterise genotypes using various molecular diagnostic tools has further confounded the cholesterol story (Bais, & Burnett, 1999). Because testing for total blood cholesterol and lipid profiles including triglycerides and lipoproteins fails to distinguish phenotypes from genotypes, it also fails to accurately predict CHD (Bais, & Burnett, 1999; Rifai, Bachorik, & Alkers, 2001). Many problems derive from uncertainties in the cholesterol story. Questions remain unanswered about the long-term effects of cholesterol lowering treatments, and the cost of treatment might not be warranted. There is evidence that the real target of aggressive drug treatments is the genotypic primary disorder known as familial hypercholesterolaemia (FH) (high blood cholesterol associated with LDL or low density lipoprotein, also known as “bad cholesterol”) that is associated with premature CHD and early death (Rifai et al., 2001, p. 482). Many cases of hypercholesterolaemia are simply manifestations of dietary and lifestyle indiscretions or due to an abundance of protective factors such as the carrier protein HDL (high- density lipoprotein and the attached cholesterol also known as the “good cholesterol”) (see also Gordon, & Rifkind, 1989). Genotypic testing in the case of cholesterol and CHD may provide the predictive function needed for management and prevention, and also assist clinicians to discriminate between diverse possible treatments. The ultimate goal of the medical interventionist approach is genetic manipulation by replacement of defective genes with functional genes. A more detailed exploration into molecular genetics will reveal however that the situation is not so simple and that the issue of gene therapy remains highly contentious and elusive. Molecular biologist Richard Lewontin (1993) challenges biologically determinist perspectives that demonstrate a commitment to the belief that all disease can be conquered by genetic manipulation. Lewontin uses the example of tuberculosis (TB) to illustrate this kind of distortion, cautioning that we must distinguish between agents and causes (p. 45). As Lewontin explains, by the time chemical therapy was introduced for TB early in the twentieth century, the death rate 212 for this disease had already declined (p. 44) (see also Dubos, 1980; Illich, 1976). The mass instigation of treatment after the fact nonetheless led to the delusion that TB had been conquered medically. As far as it can be known Lewontin argues, the decrease in death rates from the infectious killers of the nineteenth century was a result of improved nutrition and increases in real wages (p. 44). The prevention of infectious diseases requires an attack on their social, cultural, and environmental causes (p. 45). In some cases it is simple economic interests that distort the way phenomena are interpreted. As Lewontin (1993) argues, fundamental discoveries about nature and life provide not just the impetus and directions for future research, but are used in some cases to mask simple commercial relations (p. 53). An episode in the history of genetically modified corn provides a case in point. Those with the power to acquire patents, effectively gene copyright, have a monopoly on plant varieties and gain huge profits at the expense of farmers in terms of their plant variety choices and finances (p. 54). This exemplifies a situation in which a purely commercial interest successfully clothes itself in the claims of pure science, and is then taught as “scientific gospel in schools of agriculture”. Beneath pure science and so-called objective knowledge about nature, lies “political, economic, and social ideology” (p. 57). Garrety (1997, 1998) demonstrates the way knowledge and dietary recommendations relating to cholesterol, fat and CHD are “the outcome of complex social negotiations, which can only be understood in their cultural, commercial and political contexts” (Garrety, 1997, p. 727). Garrety uncovers some complex issues and ambiguous experimental results that were masked at the height of the “lipid hypothesis” era. The perspectives of many players are incorporated including scientists, advertisers, marketeers, farmers, and journalists. A full evaluation of the cholesterol test in EBLM, however, first requires critique from within the discipline, including genetics, to ensure that data and results pertaining to the cholesterol test assessment are valid. An investigator with the full range of competencies, along the continuum from “d” operational to “D” cultural and critical, can then begin to uncover the complexity of pragmatic and social relations entailed in a cholesterol test evaluation. The medical sociology literature (e.g. Petersen & Bunton, 1997) will make an important contribution towards understanding the ideological biases inherent in Western medicine, but there are other approaches. Muir-Gray (1997) pinpoints a 213 particular problem in medical information reporting, which is referred to as the “framing effect” (p. 84). Examples of the framing effect are provided in the ways that pharmaceutical companies manipulate drug evaluation data in graphs, charts and statistics, to influence clinicians to prescribe their products to patients (p. 84). Latour (1987) gives considerable attention to the way information is framed in scientific practice, in instrument data windows and printouts, and mixed graphic inscriptions, composed of tables of data, graphs, plots, equations, and statistics, that contribute to the outcomes of experiments. Latour’s main purpose is to see what happens once a scientist makes a “statement”, what the scientist must do to get others to believe it (1987, p. 21). It is not only the intrinsic quality of a scientific statement that requires exploration, but the transformations it undergoes between experiment, data acquisition and reporting (p. 59). The rhetorical function of science is not to invoke passion, style and emotion like a Sophist to win an argument, but to enlist the help of numbers, statistics and graphs to win powerful financial allies for further experiments (pp. 60-62) (see also Latour, 1990). Decisions about tests in clinical chemistry are made on the basis of graphical and statistical information (Shultz, 1999). Statistical information is by its very nature subject to manipulation, and the HTA investigator must be alert to the ideological effects and biases present in the graphical and statistical inscriptions represented in scientific communications. For “D” competence in clinical chemistry Discourse (also understood as a transdisciplinary Mode 2 knowledge system), operational and cultural criticism in the discipline is required - the validation of instruments, methods, calculations, quality and clinical interpretations (Figures 6.2 & 6.3, Levels 2-6); value-adding by evaluation, optimisation and revision of methods; and also skills in ideology criticism, and multi-literacies with laboratory inscriptions. The next two chapters demonstrate the semiotic basis of logic and pragmatics for this range of competencies in laboratory practice.

6.4 Conclusion

In this chapter, clinical chemistry is ordered within medical science Discourse under the cultural sign model juxtaposed with many other forms of Health knowledge (Figure 6.1). The structure of clinical chemistry transdisciplinary Mode 2 knowledge is simplified in the laboratory test loop to six validation activities that 214 require graphical and statistical techniques (Figures 6.2 & 6.3). The validity of clinical chemistry knowledge depends on internal scientific criteria and external pragmatic and social factors. There is a “short term syntax” of clinical chemistry knowledge demonstrated in five laboratory activities and their validation, chemical analysis systems and methods (Levels 2 & 3), data handling (Level 4), results quality (Level 5), and clinical interpretations (Levels 6). There is a “long term syntax” of clinical chemistry knowledge, demonstrated in evaluation, innovation and improvement of methods; and in laboratory test evaluations for their appropriateness, cost-effectiveness and clinical relevance in EBLM (Level 1). The laboratory test loop (Figures 6.2 & 6.3) is used as a reference in the next two chapters that demonstrate the applicability of semiotic logic and pragmatics to knowledge work and symbolic analysis in laboratory practice. In this chapter, the laboratory was analysed denotatively as a system of objects, described for its spatial arrangements and compared with industry laboratory arrangements, to illustrate the effects of laboratory reengineering, automation, robotics and informatics, on medical scientists’ roles and career prospects. The constraints of context were explored in connotative analysis, the “rhetoric” of laboratory instruments, which “speak” about the potential for de-skilling and displacement of medical scientists. This chapter concludes that to counter the de- skilling potential of black box technologies, robots, and Expert Systems, a deliberate focus of attention on knowledge work and symbolic analysis is needed in medical science education to ensure that graduates are optimally interacting with computers. As a bulwark against the displacement of medical scientists by large-scale automation, robotics and informatics, it is concluded that wider career options are needed, entailing broader competencies in symbolic analysis, and skills in social criticism as preparation for EBLM, or other avenues of employment such as clinical medicine. The specificities of “D” competence in clinical chemistry are explored in the next two chapters. Chapter 7 demonstrates the way semiotic logic applies in routine laboratory operations and in troubleshooting methods and instrument malfunctions. Chapter 8 demonstrates the way semiotic logic becomes pragmatic in different circumstances, and that rhetoric applies in laboratory test communications. Both chapters will demonstrate that multi-literacies, the manipulation and interpretation of data in graphs, charts and statistics, are crucial for operational, cultural and critical literacies and “D” competence in clinical chemistry. 215

Chapter 7 Logic in clinical chemistry laboratory practice

7.1 Introduction

The purpose of this chapter is to demonstrate that semiotic logic can be used to explain knowledge work and symbolic analysis in clinical chemistry laboratory practice; and to demonstrate that multi-literacies are used in the interpretation of laboratory data and results in graphs, charts, and statistics. Semiotic logic is demonstrated in the classification and selection of instruments for chemical analysis; in the use of instruments based on physical laws and principles that govern valid analysis, and for troubleshooting, detection, diagnosis and correction of instrument malfunctions and method errors. Knowledge work and symbolic analysis require the interrelation of theories; the integration of theories and mathematical equations in the performance of experiments; the adjustment of experimental procedures to suit local conditions, staff, space and budgets; and the extraction of information about the validity, and quality of laboratory data and results from graphs, charts and statistics. In order to demonstrate semiotic logic in laboratory practice, this chapter is organised according to the three broad categories of logic, induction, deduction, and abduction (hypothesis). Semiotic logic as derived from Peirce (1931-58) is based in signs, entailing the integration of representations and the object world with interpretations or signs in the mind (see Sections 4.3.2 & 4.3.3). The application of logic to laboratory practice is merely introductory, the complexity, contradictions and debates in logic are beyond the scope of this chapter (detailed explanations of scientific logic are provided by Black, 1952; Cohen, & Nagel, 1934; Harré; 1960; Nickles, 1980; Peirce, 1931-58; Popper, 1972/1979). The first case of inductive logic is demonstrated in classificatory activities in the selection of instruments in Section 7.2, in the ordering of chemical analysis systems into a catalogue according to physical and functional principles (Section 7.2.1). From this catalogue, one system of analysis is selected, and its technological application or instrument is subjected to analysis in the plane of expression (Section 7.2.2). The purpose of this aspect of analysis is to describe an instrument commonly used in laboratory practice, its comparison with similar but different instruments in order to demonstrate knowledge 216 work in the selection of instruments; and for identification of the significant points at which the logic of instrument use can be demonstrated (points of invariance) (see Sections 4.2.2 & 4.2.3.2). The second case of deductive logic is demonstrated as ideal use of an instrument, when everything is functional and compliant with the physical principles that govern instrument use (Sections 7.3.1 & 7.3.2). The third case of abductive logic (hypothesis) is demonstrated in troubleshooting, the detection, diagnosis and correction of errors (Section 7.3.3). It will be demonstrated in all sections (7.3.1, 7.3.2, and 7.3.3), that the use of instruments requires symbolic analysis, of the data represented in instrument windows and in graphs, charts and statistics, where the signs of optimum performance and error reside. Several sources of evidence are used in this chapter, clinical chemistry course materials; observations of practical activities in instrument printouts, and students’ practical reports. These data do not serve as evidence of students’ performance, but as reminders of the kinds of errors that can occur in laboratory practice (see Section 5.3.3.2). The levels for validation in laboratory practice, analytical systems, chemical methods, calculations, quality monitoring, and clinical interpretations as derived in Section 6.2.2.2 (Figures 6.2 & 6.3, Levels 2-6), are used as a reference in the troubleshooting section (Section 7.3.3), for knowledge work, “d” operational and “D” cultural competence in clinical chemistry laboratory practice (see Section 3.5.1). It is assumed for this purpose that the laboratory test itself is valid (Level 1), and is appropriate to apply in clinical situations. Assessment of laboratory test validity (Level 1) requires an expanded range of competencies as outlined in Section 8.2. A semiotic basis for knowledge work and symbolic analysis is summarised in Section 7.4, in which it is noted that computerised Expert Systems increasingly perform aspects of the knowledge work and symbolic analysis required of human experts. It is therefore important to make some distinctions between the capabilities of human experts and Expert Systems, and to make the knowledge work of human experts more visible, in the cultivation of knowledge workers in medical science education.

7.2 Classification of chemical analysis systems

The classification of chemical analysis systems in this section provides a catalogue of instruments, from which a commonly used laboratory instrument is selected. The classificatory schema is “induced” from analytical and clinical 217 chemistry textbooks (e.g. Burtis, & Ashwood, 1999; Holme, & Peck, 1998), and is guided by the Australasian Association of Clinical Biochemists (AACB) members’ examination requirements (Appendix A.). The term induction is used here, because textbooks and course materials commonly present information about chemical analysis systems in linear sequence, and not in a logical catalogue order (e.g. Holme, & Peck, 1998). The system of measurement, namely spectrophotometry, is selected for discussion because it is commonly used in laboratory measurements, in industry and in university teaching (Burtis, & Ashwood, 1999; Appendix B.) (note that this system of measurement applies to most of the practical procedures listed in Appendix B1., but they are named according to the test and not the instrument). Spectrophotometry is applied to laboratory measurement with the aid of instruments called “spectrophotometers”, of which there are several variants. Spectrophotometers make use of the interactions between light and matter in order to measure sample components, for example blood glucose as an aid to diagnosis and monitoring of Diabetes Mellitus; and blood urea in diagnosis and monitoring of kidney disease. The selection of instruments at the undergraduate level is restricted to concerns about the suitability of particular instruments, for particular measurements so that choices are logical, based on theory, and aimed at analytical validity of methods and instruments (Figure 6.3, Levels 2 & 3). Analytical validity is determined in accuracy and precision studies of method and instruments, which reflect the sensitivity of instruments to respond adequately to components (analytes) of interest; to detect analytes at particular concentration levels; and methods are designed to minimise interferences (Holme, & Peck, 1998, p. 19). Pragmatic issues such as availability of instruments, space, staff and budgets, the concerns of laboratory management and postgraduate studies (Appendix A.), are considered in Sections 8.2 and 8.3 in terms of the value-adding competencies that apply in clinical chemistry laboratory practice. It is the purpose of Section 7.2 to demonstrate knowledge work in the selection of instruments, which first requires the knowledge worker to set up a catalogue of chemical analysis systems. Although there are not many systems from which to choose, the activity can be explained in terms of inductive logic, of the kind used for ordering objects into general categories, families, genera, species based on their differential features once they have been subjected to analysis by division. The same structuralist principles, division, classification and system can be applied to laboratory instruments, as they were applied by Linnaeus in natural history (Black, 218

1952; Cohen, & Nagel, 1934; Foucault, 1966/1970; Harré, 1960); in linguistics (Hjelmslev; 1943/1961; Saussure, 1959), and to objects in general (Barthes, 1964/1973, 1967/1990). A classificatory schema for chemical analysis systems and instruments can be derived from the array of systems of chemical analysis described in analytical chemistry textbooks, based on their physical principles (e.g. Holme, & Peck, 1998). In the Peircean schema, a catalogue is induced from a particular perspective, indexing description of the object in question, from an unstructured set of options ordered into logical categories (see Section 4.3.3). The common systems of analysis used in clinical chemistry can be classified into four main groups, spectroscopy, electrochemistry, immunochemistry, and separations chemistry (Figure 7.1). Each system can be divided further based on significant response variations identified when their respective applications or instruments have been subjected to analysis by division (expression line, see Section 7.2.2.1). Laboratory instruments can be ordered into classes according to functions based on physical theory and the requirement for sample preparation by different separations techniques. The chemical analysis systems placed on the left side of Figure 7.1 are based on direct measurement of an interaction or physical property, spectroscopy and electrochemistry respectively (following the removal of interferences) (Holme, & Peck, 1998). The chemical analysis systems placed on the right side of Figure 7.1 are indirect methods. In the case of immunoassay, measurement of the response of radioisotopes, fluorophors or enzymes attached to the antigen-antibody complex of interest is measured (Holme, & Peck, 1998, p. 227). In the case of separations, the sample is subjected to extraction, purification or separation to identify component compounds or elements based on their polarity (solubility characteristics), charge, size or mass, using electrophoresis, chromatography, mass spectrometry and ultra- centrifugation techniques (Holme, & Peck, 1998, p. 91). The separations products are then subjected to a measurement system such as spectrophotometry. As in all classifications, some systems cannot be neatly placed in the schema. For example, nephelometry and turbidimetry are direct spectroscopic immunoassay techniques, and mass spectrometry performs both separations and measurement. DNA technologies and specialised chemistry instruments such as Nuclear Magnetic Resonance spectroscopy (NMR) are omitted because they are not as yet commonly included in clinical chemistry testing.

219

CHEMICAL ANALYSIS

SEPARATIONS SPECTROSCOPY

IR UV-VIS Polarity Ionic Size/Mass

MAS MFS AAS FES

Nephelometry GLC LLC LSC IEC Turbidimetry EP IEF

HPLC CEP CIEF

Chromatographic Electrophoretic

Ultracentrifugation Mass Spectrometry

ELECTROCHEMISTRY IMMUNOCHEMISTRY Potentiometry Biosensors Precipitation Immunoassay ISE Nephelometry RIA EIA FIA LIA Turbidimetry Coulometry Voltametry MEIA FPIA Polarography Chemiluminescence

IR infra red GLC gas liquid C IA immunoassay UV-Vis ultra violet – visible LLC liquid liquid C RIA radio IA LSC liquid solid C EIA enzyme IA MAS molecular absorption IEC ion exchange C FIA fluorescent IA MFS molecular fluorescence HPLC high pressure liquid C LIA luminescent IA AAS atomic absorption MEIA micro particle capture EIA FES flame emission EP electrophoresis FPIA fluorescence polarisation IEF iso-electric focussing IA ISE ion selective electrodes CEP capillary EP CIEF capillary IEF

Figure 7.1. Chemical analysis systems.

In knowledge work decisions are made about which system of chemical analysis to choose based on knowledge about the physical properties of the analyte of interest, and whether it responds to light or Electro Magnetic Radiation (EMR) by absorption, emission, fluorescence, reflection or light scatter; and whether the instrument is sensitive enough to detect the level of concentration of the component of interest (Appendix A.; Holme, & Peck, 1998, p. 91). If significant interferences are present, the knowledge worker will consider which system of purification and/or 220 separation is best suited to render the sample into a suitable state for analysis, chromatographic techniques for example (Holme, & Peck, 1998) (Figure 7.1). Spectrophotometry is given specific attention in Section 7.2.1 because it is the form of chemical analysis most commonly used in clinical chemistry. Whereas the specific form of molecular absorption spectrophotometry (MAS) provides the focus of structural analysis in the plane of expression (Section 7.2.2), all other systems of chemical analysis are amenable to similar treatment. Before analysis of MAS in the plane of expression, the system of chemical analysis, spectrophotometry requires brief explanation.

7.2.1 Spectrophotometry

Spectrophotometry is the principle system of chemical analysis used in clinical chemistry laboratories. It is an application of substantive physics content, EMR, in the formation of the unstructured phenomenon light, and is given expression (form and substance) in the instrument, the “spectrophotometer”, which records interactions between elements and compounds of matter (e.g. blood plasma components such as sodium and glucose) and EMR, for the purpose of acquiring diagnostic information about patients (Figure 7.2a).

Phenomenon Light

C = content Physics E = expression EMR = light ≠ electricity ≠ magnetism S = substance F = form S T = Waves ≠ particles ≠ waves & particles C T = theory F E F Spectro instrument forms FES ≠ AAS ≠ MAS ≠ MFS S Technological applications Spectrophotometry ≠ potentiometry ≠ mass spectrometry (light) (electricity) (magnetism)

Purport/Matter/Continuum

Figure 7.2a. EMR cultural sign model.

The spectrophotometer is essentially two systems of analysis combined into one. Spectroscopy is the study of the absorption and emission of radiation by matter 221 at specific wavelengths, used to identify and quantify substances; and photometry allows more precise measurement or quantification of the intensity of light emitted, absorbed or transmitted (Holme, & Peck, 1998; Meloan, 1968a; Rosenfeld, 1999; Vogel, 1961). As in any measurement system, the validation of data arising from the use of spectrophotometric instruments requires at least a rudimentary knowledge of the nature of the interactions between EMR and matter, and hence certain levels of attainment in physics and chemistry are entry requirements for studies in clinical chemistry (see QUT handbook, n. d. for pre-requisite subjects, Bachelor Applied Science [Medical Science]). The purpose of this discussion of spectrophotometry is simply to locate it within the substantive structure of physics as the pre-requisite knowledge needed for operating effectively in clinical chemistry for the validation of instruments and methods (Figure 6.3, Levels 2 & 3). Understanding of the properties of EMR and its interactions with matter is essential in the selection of appropriate instruments, optimisation and the development of new methods, and in the validation of data acquired from measurement (Holme, & Peck, 1998, pp. 2-4). The linguistically based structure of the phenomenon light (Figure 7.2a) is used to direct object analysis of the spectrophotometer in the plane of expression (in Section 7.2.2). Spectrophotometry also requires analysis in the plane of content, the elements of which co-exist in mutual correlation with elements in the plane of expression. A comprehensive analysis of the theoretical structure of EMR is beyond the scope of this chapter, so that only those theoretical features pertinent for validation of spectrophotometric analysis are selected (examples of the structural analysis of physical theories are provided by Balzer, Moulines, & Sneed, 1987, & Balzer, & Moulines, 1996). Put simply, analysis of EMR in the plane of content would place all theories of light around the core principle EMR (substance of the content), in a structured network of its various formations (Figure 7.2b). There are significant differences between the two structures (Figures 7.2a & 7.2b). In the linguistically based model (Figure 7.2a), verbal and metaphorical analogies are used to describe light theories in terms such as “corpuscles”, “waves”, “particles”, “waves and particles” (see Davies, 1995 for discussion of wave-particle theory). In a scientific theory-based model, the technological applications and mathematical equations applied to theories are also considered (Figure 7.2b). For the purposes of this chapter it is sufficient to isolate some key points about EMR that are needed for integrating theory and practice in the use of spectrophotometers. Theory and 222 applications are integrated in equations, therefore to understand and use spectrophotometers effectively, mathematical equations are needed.

T Theory Purport/continuum/phenomenon Light M Mathematics Substance of the content Physics A Applications EMR Technology

T 2 Electricity Magnetism R = 1, b0= 0 Light

/linear/ /hyperbolic/ M Faraday Oersted Maxwell Planck E = - ∂B/∂t E = hc λ A T [C] [C] A Electrochemistry Mass Spectrometry Spectrophotometry

Beer Lambert Equation M Nernst Equation Beer Lambert law o A = εcL E = E -RT ln a m/z = H2R2 A = εcL nF 2V Forms of expression

Figure 7.2b. EMR theory net/content.

There are three equations to consider in spectrophotometry, two are theoretical and one is applicable in spectrophotometric calculations (only two of these equations, one theoretical and one for applications, are represented in Figure 7.2b). There are two theoretical equations relevant in spectrophotometry, applied to theories of light in terms of waves, “c=λν”, which accounts for the frequency “ν” and wavelength “λ” of light (where “c” = the speed of light); and particles, “E = hc/λ”, which describes EMR as particles having discrete packets or quanta of energy (“E”) (for details see Holme, & Peck, 1998, pp. 36-39) (see also Meloan, 1968a; Vogel, 1961). For the purposes of spectrophotometric measurement, a crucial point is highlighted in the juxtaposition of these two equations. The specificity of the spectrophotometric measurement situation depends on light energy as well as its frequency or wavelength. This is because the interactions between EMR and elements or compounds occur only at specific light energies and hence wavelengths. Depending on the circumstances of the interaction, light absorption, transmittance, fluorescence, emission or reflection occurs in the ultra-violet (UV) and visible (Vis) regions of the electromagnetic spectrum (EMS) (∼200-760nm) (Holme, & Peck, 223

1998, p. 39). The optimum wavelength, referred to in experiments as “λ-max”, is required for a maximum response from the compound of interest, and is therefore a crucial factor in assumptions about the validity of data acquired in MAS experiments (e.g. Appendix B2.). Measurements taken away from “λ-max” are likely to produce inaccurate and imprecise data and invalid results. The validity of spectrophotometric data and calculations are thus dependent on the correct wavelength of light, for application of the third equation in calculations, “A=εcL”, based on the Beer- Lambert Law which demonstrates a constant relationship between light absorption (Absorbance, “A”) and concentration of sample (“c”) within certain limits (this equation is explained further in Section 7.3.1) (Holme, & Peck, 1998, p. 49). Note that the left side of Figure 7.2b represents the different forms of expression, verbal, mathematical, graphic, and statistical, applied to the “A vs c” relation in the Beer- Lambert equation. The ability to switch between different forms of expression is discussed further as a major aspect of symbolic analysis in Section 7.4. The Beer-Lambert Law applies specifically to the use of the molecular absorption spectrophotometer (MAS) which is a member of the family of spectrophotometers, each member having a specific principle of operation and application (Figure 7.1). Knowledge of the components of instruments is needed in order to assess the validity of instrument use (Figure 6.3, Levels 2 & 3), and in order to select the appropriate instrument in different analytical situations (Holme, & Peck, 1998, p. viii). Understanding of fundamental principles is required for knowing when alternative applications are needed, and when samples must be purified and modified to render them amenable to the selected form of analysis (p. 2). The structuralist method for analysis of objects in the plane of expression is demonstrated in the next section in the ordering of the MAS by its division into component parts, and for the identification of significant points of invariance from which, by relations of virtual association, comparisons with other spectrophotometers are made.

7.2.2 MAS in the plane of expression

For the purpose of demonstrating knowledge work in the selection and use of laboratory instruments, there are three aspects of semiotic analysis of MAS in the plane of expression to consider. Firstly, the instrument is described for its 224 morphological characteristics and arrangements, by its analysis or division into component elements (expression line) (in Section 7.2.2.1) (see also Sections 4.2.2 & 4.2.3.2). From the expression line, significant points of invariance are identified for the second and third aspects of analysis. Secondly, the points of invariance identified in the expression line of MAS are used, based on associative relations of similarity and difference, for the ordering of spectrophotometric instruments in the expression side, as a class in the catalogue of chemical analysis systems (Figure 7.1). Thirdly, knowledge work is demonstrated in the selection of instruments by the navigation of the pathways defined in the semantic field of mutual correlations between the plane of expression and plane of content based on theory, technological possibilities and other more pragmatic concerns (in Section 7.2.2.2). The points of invariance identified in the expression line, are used to demonstrate semiotic logic (deductive and abductive) in the use of instruments, and for troubleshooting experimental errors (in Section 7.3). The principal data sources used as evidence in the analysis of MAS in the plane of expression are the MAS instrument; textbooks providing information about MAS and other instruments for comparison with MAS (e.g. Holme, & Peck, 1998; Meloan, 1968a; Skoog, & Leary, 1992; Vogel, 1961); and manuals of instrument part specifications (e.g. Human, 1985). Knowledge work is guided by semiotic theory, clinical chemistry knowledge and AACB membership requirements (Appendix A.).

7.2.2.1 The MAS expression line

In this section, the MAS instrument (Figure 7.3) is described for the spatial arrangements of its component parts, in order to identify points of invariance for comparative analysis in the selection of instruments (in Section 7.2.2.2), and for the logical use of instruments (in Section 7.3). There is no intention however, to supply a detailed textbook description of the spectrophotometer, which has been accomplished by various experts on the subject (e.g. Holme, & Peck, 1998; Meloan, 1968a; Skoog, & Leary, 1992; Vogel, 1961). The MAS as used in practical classes for experiments is a relatively small bench top analyser (Figure 7.3), used to measure the interactions of molecules in the solution of a reaction mixture derived in an experiment, at a specific wavelength of light (e.g. Appendix B2.). There will be more than one way to divide up an instrument, but for educational purposes functional 225 principles are important. The key components of MAS are explained by its dual function combining spectroscopy and photometry, spectroscopy for EMR selection, and photometry for detection of EMR transmission (Holme, & Peck, 1998, p. 60).

Data window

Mode Wavelength Reference Sample input

Figure 7.3. Pharmacia Biotech Ultraspec UV/Visible Spectrophotometer.

MAS can also be divided into two broad categories of parts, the first category representing the fundamental principles of operation as defined by its dual function, light absorption and response detection, and the second category, associated with refinements, the complex system of slits, mirrors and lenses (collimating and focusing) which permit reduction of the size of the instrument, and improve its performance (specificity) by focusing and narrowing the bandwidth of light (Holme, & Peck, 1998, p. 72). Because stray light must be eliminated the MAS system is housed in a black box semi-automated assembly. This is essential for optimum performance of the instrument, and adjustment of instrument refinements is largely out of the control of the operator (Figure 7.4a). The isolation of points of invariance, the points that are theoretically significant, is therefore directed towards those operational parts that are to varying degrees under the control of the user. There are five component MAS parts selected in this section for their theoretical significance, as represented in the “white-box” MAS model (Figure 7.4b) (adapted from Holme, & Peck, 1998, p. 60), and its juxtaposition with other forms of 226 spectrophotometry, molecular fluorescence (MFS), nephelometry and turbidimetry, flame emission (FES) and atomic absorption (AAS) (Figures 7.4c-f).

Measurement mode A or %T POV 1 2 3 45 Wavelength Reference Lamp Monochromator Cell Detector Readout

λ AR 0.600 0.500 adjustable diaphragm Sample Data input output POV = point of invariance (a) Black box spectrophotometer MAS (b) White box spectrophotometer MAS

POV 1 2345 POV 1 2 345 Lamp Monochromator Cell Detector Readout Lamp Monochromator Cell Detector Readout Excitation light Turbidimeter

10.00 0.890

Emission light Nephelometer

(c) Molecular Fluorescence Spectrophotometer MFS (d) Nephelometer and Turbidimeter

POV 1 & 32 4 5 POV 1 & 2 342 5 Flame Filter Detector Readout Light source Flame Filter Detector Readout

HCT Hollow Cathode Tube 0.200 145.0 Sample input Sample input (e) Flame Emission Spectrophotometer FES (f) Atomic Absorption Spectrophotometer AAS

Figure 7.4. The class of spectrophotometers.

The white-box MAS model foregrounds the significant points of invariance based on the physical principles that define MAS use, such that all parts and refinements not relevant to the analysis are removed (see Section 6.3.4.1 on the black-box/white-box conceptual opposition). By foregrounding these five key 227 elements, the focus of attention is drawn directly to the significant points of invariance, also referred to as the point of variation (POV) at which different physical principles of analysis are signified. The semiological principle defined by Barthes (1967/1990) for object analysis, the signifying matrix, object, support, variation (OSV) (see Section 4.2.3.2), is used in this section to identify the points of the MAS instrument that have theoretical and practical significance. Non-linguistic objects are analysed for the relations of combination of their component elements (assembly in logical sequence in the expression line, A • B • C etc.). The component parts of an object (O) identified by analysis are merely supports for the signification (S), because work must be done in drawing out their analytical significance, which is constituted by the invariance principle (V). The support “S” for a signification is thus the material aspect of an object that can be varied to significant effect “V”, the point from which the signification emerges, hence referred to as the point of invariance (POV) as marked in the Figures 7.4b-f. For the purpose of integrating theory and practice, the signifying matrix “OSV” has a counterpart in the Peircean sign triad, representation- object-interpretation (ROI) (see Section 4.3.2). In both cases, the signifying matrix and the sign triad, an act of interpretation is required to draw meaning or significance from an object in question. In the case of the sign triad, the nature of the interpretation in logic is also considered. For example, the response of the MAS instrument constitutes a source of invariance, signifying molecular absorption, which stands in conceptual opposition to similar but different spectrophotometric responses, fluorescence, scatter, reflection and emission (Figures 7.4b-f). The valid use of the instrument is logically deduced and error diagnoses are abduced by consulting the Beer-Lambert Law (in Sections 7.3.1 & 7.3.3). A brief explanation of the significant points of invariance (POV) derived from analysis of the MAS in the expression line follows as preliminary to demonstrating logic in instrument use and in troubleshooting errors. The MAS is divided in the expression line into a set of articulated components co-existing in relations of combination numbered from left to right (Figure 7.4b, POV1-5) in the spectrophotometric process: Lamp • monochromator • sample cell • photodetector • readout-meter. These five points of invariance (POV) are theoretically significant, and are the points at which alternative component parts 228 and alternative instruments emerge by association because they are similar but different. POV 1 is the light source or lamp, providing light in the UV-Vis region of EMS (Holme, & Peck, 1998, p. 61; Human, 1985, p. 9) (Figure 6.4b). In knowledge work, the response of atoms and molecules to different energies or wavelengths is considered, whether the sample preparation has colour, and therefore which lamp is the appropriate one to choose (e.g. hydrogen or deuterium for the UV region [∼200- 380nm], tungsten for the Vis region [∼400-760nm] and xenon for the full range of UV-Vis wavelengths) (Holme, & Peck, 1998, p. 61; Tiffany, 2001, p. 77). POV 2 is the monochromating device for the selection of specific wavelengths or bandwidths at which the molecules of the substance of interest absorb maximally and with minimal interference (e.g. Appendix B2.). POV 2 requires operator intervention because wavelengths are selected from the broad bands of light supplied by the lamps. Although there are several components to a monochromating device, including entry and exit slits, collimating lenses, wavelength selection or dispersion device, focusing lenses and mirrors, the main point to consider is wavelength selection (Holme, & Peck, 1998, pp. 61-67). As explained in Section 7.2.1, a maximum response by the sample occurs at an optimum wavelength (λmax) to ensure accuracy of measurement and compliance with the Beer-Lambert Law. There are several types of monochromating device with similar functions but different approaches, with varying degrees of specificity, ranging from glass filters with wide bandwidths (~20-40nm) to a range of devices such as interference filters, prisms, and diffraction gratings providing much lower bandwidths (as low as 0.1nm in some cases). An alternative approach, the photodiode, incorporates wavelength selection in the detection device (see POV 4 below). The monochromating device is fixed with each instrument and its specification in terms of instrument precision, dictates, along with economic circumstance, the suitability of the instrument for different purposes (Human, 1985). The approach to wavelength selection is thus an important consideration in the selection of instruments. In some circumstances a broad bandwidth will suffice, in other circumstances a narrower bandwidth is required but is more than likely the more expensive option. MAS may not produce the most precise and accurate results in all circumstances, although it may suffice in constrained economic circumstances. 229

POV 3 is the cell or cuvette that contains the sample for placement in the instrument (Human, 1985, pp. 12-13). For manual instruments operator control is greatest at this point, as is the opportunity for operator error, in cell material selection (glass, plastic and quartz crystal), in cell placement in the instrument (alignment in the path of the light), in cell handling (fingerprints, drips, scratches), or there may be errors in the reaction mixture placed in the cell (illustrated in Section 7.3.3.1). POV 4 is the detector, the point at which light is converted to an electrical signal made possible because the detector responds to light by the displacement of electrons, thus creating an electrical potential that can be measured by a voltmeter (Holme, & Peck, 1998, pp. 67-70). There are various kinds of detector including phototubes and photomultipliers, and photodiodes assembled in diode arrays permitting response to the entire UV-Vis spectrum and precluding the need for POV 2, the monochromating device (Holme, & Peck, 1998, p. 104; Human, 1985, p. 14) (see also Varcoe, 2001). The detector is fixed and requires no operator intervention, except for consideration if there is instability in the readout meter that sometimes occurs due to detector deterioration or voltage fluctuation. The type of detector is considered in instrument selection in terms of specificity and sensitivity, accuracy and precision, and also cost and availability of parts. In making the choice there is a trade off between sensitivity and specificity that is, between specificity of light (wavelength selection) and sensitivity in photometric detection (Human, 1985, p. 16). POV 5 is the signal handling and measurement system, the point at which data are given in the selected mode, transmittance (%T) or absorbance (A), from which a result is calculated based on the Beer-Lambert equation (explained in Section 7.3.1). This may involve a simple meter indicating an absolute value of the output signal (analogue), or its digital conversion by a light emitting diode (LED) which decreases ambiguity in the interpretation of readings (Human, 1985, p. 15). Both choices of raw data output, “%T” and “A” are given in basic MAS, and instruments now incorporate microprocessors that convert readings to concentration units. In some cases, the MAS is a scanning instrument with a chart recorder attached for recording real time reactions, “A” versus time, and spectral profiles, “A” versus wavelength (e.g. Instrument printouts, Appendix C.). POV 5 is not a frequent source of operator error because data are almost invariably handled in the “A” mode as specified in practical protocols (e.g. Appendix B2.). Data retrieved at this point are 230 transformed, represented graphically and calculated, a function that is computerised in industry laboratories (discussed in Section 7.3.2). In summary, the five points described as points of invariance (POV), lamp, monochromator, cell, detector, readout, are those at which, by association, relations of similarity and difference bring to light alternative components, and alternative modes of analysis for different analytical purposes. These options are explored in the expression side for the selection of instruments in the next section.

7.2.2.2 The expression side and instrument selection

This section demonstrates the knowledge work entailed in the selection of instruments by comparative analysis of instruments; entailing analysis of the expression side of MAS by its virtual associations with similar but different instruments; and by the navigation of expression-content relations in a semantic fragment of MAS knowledge (see Section 4.3.5). The ability to select appropriate modes of analysis is a requirement for membership of the AACB (see Appendix A.). By applying a semiotic framework to the selection process, the knowledge work that is otherwise the “invisible craft” of an expert, is made visible (taking the cue about the visibility of craft knowledge from McGee, 2002, p. 1). This is presumably the kind of information designers of Expert Systems (requiring knowledge base plus inference engine or rules of connection) have difficulty extracting from experts (see Chi, Glaser, & Farr, 1988; Gillies, 1996; Jackson, 1999, for in depth discussion of this problem). Three aspects of semiotics are used to make the process of instrument selection visible, the cultural sign model for the comparison of different classes of chemical analysis systems (Figures 7.1 & 7.2a); the comparison of forms within the class of spectrophotometers by analysis in the expression side emerging from the points of invariance in the expression line in the plane of expression (Figures 7.4a-f); and the mutual correlations each expression form enters into with culturally coded contents in the plane of content, in semantic fragments according to theory and the circumstances of analysis. The first approach, beginning with the MAS form, considers the comparison of chemical analysis systems from the perspective of the invariant “response”. Thus comparisons are made among different systems of chemical analysis as well as within the same class of chemical analysis systems, for example spectrophotometers. 231

The MAS as an application of the substance of the content, EMR, stands in conceptual opposition to the alternative substance of the content, electricity. Although EMR and electricity are related physical phenomena, in clinical laboratories they are distinguished in terms of a response (light absorption and emission) versus a property (electrical conductance potential, current, or activity). Electricity thus stands in conceptual opposition to EMR (EMR/electricity or EMR ≠ electricity), so that <> becomes substance for another analysis, and likewise, for mass spectrometry with respect to magnetism (Figure 7.5) (see also Figures 7.1 & 7.2a). Electrochemistry and mass spectrometry can be subjected to the same treatment as MAS in the plane of expression.

From Figure 7.2a EMR ≠ electricity ≠ magnetism

S Spectophotometry ≠ Electrochemistry ≠ Mass Spectrometry C F E Expression line F S Lamp • monochromator • cell • detector • readout Supports

From Figure 7.4 1 Light 2 λ 3 & 4 Response 5 Mode Variation

//MAS// =<> Objects ≠ //MFS// =<> ≠ Expression side //Nephelometer// =<> ≠ //Turbidimeter// =<> ≠ //FES// =<> ≠ //AAS// =<> Go to Figure 7.6

Figure 7.5. MAS in the plane of expression.

The second aspect of analysis is conducted within the class of spectrophotometers in order to make the appropriate spectrophotometer selection. The analysis is conducted in the expression side, which emerges according to the associative relations arising at the significant points of invariance in the signifying matrices in the expression line (referred to also as syntactic markers [sm] in Section 4.3.5.2). A signifying matrix requires an object, a support for the signification, the POV from which the signification emerges. In the case of spectrophotometry, the supports of signification at POV 2 (monochromator) and POV 4 (detector) can be 232 varied, but are held constant in a particular instrument, so that significance is determined in instrument selection by consideration of the interaction between the sample cell in POV 3 and EMR from POV 1 and POV 2 (Figure 7.5). The interactions between EMR and matter (elements and compounds) produce variations in response that are similar but different in theoretical terms. The invariant response, constitutes the differences in the family of spectrophotometers, absorption and transmission (Figures 7.4b & 7.4f), fluorescence (Figure 7.4c), light scatter (Figure 7.4d), and light emission (Figures 7.4e & 7.4f). The MAS configuration (or morphology) (Figure 7.4b) signifies analysis of compounds in solution (molecules) responding to light with molecular absorption which, by association, signifies three more instruments associated with molecular interactions with EMR. Two instrument variants are nephelometry and turbidimetry used for turbid molecular solutions involving the scattering of light from particulate matter in the case of nephelometry, and the reduction of light transmission in the case of turbidimetry (Figure 7.4d). In addition to the response variant, instrument morphology can also be varied. The nephelometer is configured with the detector POV 4, displaced at 90º or other angle to avoid interference from the incident beam. The turbidimeter is configured in the same way as the MAS, because absorbance or transmission is measured (Holme, & Peck, 1998, p. 238). A third variant in molecular spectrophotometry involves molecular fluorescence (MFS), emitted from specimens at longer wavelengths than the source radiation, and hence necessitating separation of the excitation and emission wavelengths to avoid false readings at POV 5. The instrument is thus configured in the same way as a nephelometer, but the response is different, fluorescence as opposed to light scatter (Figure 7.4c). Two other variants of spectrophotometer involve interactions with EMR and atoms or elements. In the case of flame emission spectrophotometry (FES), atoms of the element for analysis (e.g. sodium, potassium and lithium) are thermally excited in a hot flame to which they respond by light emission (Figure 7.4e) (Holme, & Peck, 1998, p. 77). Most atoms however do not give an adequate response to heat in this manner but absorb light at wavelengths specific for each element (e.g. copper, zinc, lead, and arsenic), in Atomic Absorption Spectrophotometry (AAS) (p. 80) (Figure 7.4f). Although these configurations signify variations in the class of spectrophotometers, their full significance emerges once the plane of expression is considered in mutual correlation with the plane of content, according to the theoretical content of each instrument, the 233 circumstances of measurement, characteristics of the analyte in question, the sensitivity of the instrument, interferences, and also pragmatic factors that dictate the selection of instruments (Figure 7.6). Because multiple significant units comprise a spectrophotometer, each in turn being structured, a global semantic field would be needed to represent the entire meaning system of possible connections in chemical analysis. It is impossible to represent such a structure, and it is necessary to work with pertinent fragments, beginning with a single object, the MAS, as demonstrated in the third aspect of analysis.

From Figure 7.5 <> ≠ <> <> <> ≠ <> Context 1 Circ 1 nature of substance Expression line ≠ Theory //MAS//…Sm = <> Element ≠ Compound ≠ //MFS// Circ 2 response ≠ //Nephelometer//  Context 2 Absorption ≠ Measurement ≠ //Turbidimeter// Fluorescence ≠ ≠ //FES// Scatter Circ.1 concentration ≠ ≠ //AAS//  Emission Circ 2 Interference -3 Expression side mmol 10 Context 3 ≠ Lab management µmol 10-6 ≠ nmol 10-9 ≠ pmol 10-12

Go to Figures 7.11, 7.16 & 8.2

Figure 7.6. MAS semantic fragment.

The third aspect of analysis demonstrates the way a knowledge worker can short circuit the global semantic system by isolating a pertinent fragment of MAS expression-content relations, using semantic markers, contextual and circumstantial selections (denotations and connotations) to guide the selections of pathways chosen (see Section 4.3.5.2). The analysis begins with the object (SV) //MAS// which stands in relationships of similarity and difference to other SV, //FES//, //AAS// and so on, each with their own semantic fragments, and the class of spectrophotometers in turn stands in opposition to other classes of chemical analysis systems such as <> and <> (Figures 7.1 & 7.5). Each SV can be 234 represented in a plane of expression reducible to elements that become other SV that in turn participate in articulations with other SV, their selection being guided by theory, and the context and circumstances of the analysis. There will therefore be many connections in the semantic system to consider in instrument selection. Only a few contextual and circumstantial selections are made to illustrate how this works (Figure 7.6). When confronted with the need to choose an instrument for the analysis of analytes, for example, glucose, sodium, potassium, lithium, lead, copper, arsenic, and steroids, several factors will be considered (refer to Burtis, & Ashwood, 1999, for details of each system and its applications). In the context of theory, consider the nature of the analyte, whether it is a compound, glucose for example, or whether it is an element, sodium, potassium, lead and arsenic, for example, because that will dictate which response option to consider. At the same time consider, in the context of measurement, if the analyte absorbs light, fluoresces, emits light following thermal excitation, reflects, refracts or scatters light owing to its particulate composition, and at levels at which responses can be detected. Thus knowledge about the theory of the physical response and the performance of each measurement system and chemical method is considered, including knowledge about the sensitivity of the instrument and method interferences (Figure 6.3, Levels 2 & 3 validity). Knowledge of the levels of concentration of analytes expected in the specimens will assist the decision process. If the analyte in question is the compound glucose, which is present in blood at millimolar concentrations (molar x 10-3) MAS will be considered. If the compound is a steroid however, present in blood at nano- molar concentrations (molar x 10-9), it will not be detectable by MAS, but if it possesses aromatic properties and tends to fluoresce, MFS might be considered. On the other hand, interferences may be present that quench fluorescence, so that alternatives such as HPLC (High Performance Liquid Chromatography), immunoassay and mass spectrometry are considered. If the analyte in question is an element such as sodium, potassium or lithium, present at millimolar concentrations, it can be subjected to thermal excitation and its light emission measured using FES. It the element is lead, copper or arsenic, present at much lower micro-molar concentrations (molar x 10-6), it will not exhibit an adequate response to heat, but will absorb light of a specific wavelength, and AAS can be considered. If the sample is turbid, due to an antigen-antibody reaction, then light scatter can be measured using nephelometry or light transmission using turbidimetry. This process continues 235 ad infinitum and stops once the knowledge worker makes a decision, being satisfied that all the options have been considered. In summary, from the semiotic perspective, in knowledge work, decisions are made in the selection of instruments, by consideration of physical laws and principles that underpin measurement systems and chemical methods which are subject to the least interferences. The MAS denotes molecular absorption in the interaction between EMR and molecules in solution at specified maximum wavelengths (λmax), and suitability for quantification of compounds in solution such as glucose present at millimolar concentrations. In knowledge work the limitations of MAS will be considered in terms of specificity of wavelength and sensitivity of response as indicated by instrument manual specifications (e.g. Human, 1985). Different MAS will vary in the quality of their component parts, according to the specificities of monochromator wavelength selections (POV 2), and photometric sensitivities of photodetectors (POV 4), which confer different levels of performance or accuracy and precision on each instrument. The performance of even the best components in MAS will however be inadequate for every type of analysis. Consideration of alternative instruments in the expression side, and the semantic system will direct the knowledge worker towards the instrument of higher analytical performance. What appears to be an optimum selection of an instrument on the basis of physical principles, specificities and sensitivities (theoretical considerations) however, may not be appropriate for all contexts and circumstances (pragmatic considerations). In the context of laboratory management there will be certain constraints including staff, space, costs, time, and the availability of service and parts in the selection of instruments. The global semantic system is expanded for this purpose in Section 8.3. In conclusion to Section 7.2.2.2, the analysis of the MAS in the expression line describes the instrument’s components and the MAS theoretical relations with other instruments, spectrophotometric and other chemical analysis systems, in the expression side. The expression line and expression side together make up the plane of expression that is in turn placed in mutual correlation with the plane of content from which a pertinent semantic fragment is navigated in the selection of instruments. The semiotic method has thus been demonstrated as one way to make knowledge work in the selection of instruments visible. The claim that semantic fragments are mapped in this manner by experts is based on the assumption that 236 knowledge can be structured in the mind around signs, representations, objects and interpretations participating in an infinite progression of signs but short circuited in semantic fragments for specific purposes (see Sections 4.3.2 & 4.3.5). The logic that applies to the way such knowledge structures are used is explored in the next section.

7.3 Logic in instrument use and troubleshooting errors

The semiotic logic of knowledge work in the use of the Molecular Absorption Spectrophotometer (MAS), and in troubleshooting errors, is demonstrated in this section. Deductive logic is applied to the use of the MAS in ideal error-free situations (Section 7.3.1); and the validity of data outputs are assessed for compliance with the physical rule, the Beer-Lambert Law, that governs the ideal use of MAS, as it is represented in equations, graphs and statistics (Section 7.3.2). Abductive (hypothetic) logic is applied in error detection, diagnosis and correction, otherwise called troubleshooting (Section 7.3.3). In the case of deductive logic, the ideal or expected method performance is drawn from textbook information (e.g. Burtis, & Ashwood, 1999; Holme, & Peck, 1998), and some experiment demonstrations are taken from the teaching situation (e.g. Appendices B2.-B4.). In order to demonstrate abductive logic, error scenarios are described on the basis of what would necessarily occur in the ideal situation, and examples of errors are drawn from observation data of teaching laboratory practical classes, as captured in instrument printouts; and students’ practical reports in which errors in data handling are recorded in graphs and statistics (see Sections 5.3.1, 5.3.2 & 5.3.3.2). For the purposes of knowledge work and symbolic analysis, the use of laboratory instruments is considered in terms of five of the six validation levels derived in Section 6.2.2.2 (Figures 6.2 & 6.3), the validity of analytical measurement systems, instruments and chemical methods (Levels 2 & 3), the validity of data and results (Levels 4 & 5), and their clinical interpretations (Level 6). Validation is determined at each level using different combinations of graphs, charts and statistics, which means knowledge work requires symbolic analysis. Before proceeding with logic in instrument use and troubleshooting errors, three methodological concerns are addressed. Firstly, it is not signs in their representative aspects, icons, indexes, and symbols that are considered, but logic, sign action or semiosis, in the integration of the interpreting aspects 237

(Representations), the pre-interpretive aspects (Objects), and the interpreted aspects (Interpretants) of signs (see Section 4.3.2). Secondly, the signifying matrix, object, support and signifying variation (OSV), also referred to as the point of invariance (POV), is the point from which the signification or interpretation emerges (see Sections 4.2.3.2 & 7.2.2.2). In the use of an instrument, a representation, an object and an interpretation must be considered. Each act of interpretation in troubleshooting errors requires a tacit integration of a pre-interpretive object, for example a symptom or clue, as represented in instrument data windows, graphs, charts and statistics, and interpreted as a sign of non-compliance with the rule, the Beer-Lambert Law and therefore a sign of error and invalid data. Thirdly, representations are integral in every aspect of laboratory practice. In symbolic analysis multi-literacies are used in manipulations of the many forms of expression or representation in which laboratory data and results are given. This is demonstrated throughout the next three sections, Sections 7.3.1 and 7.3.2 for ideal rule-governed use and data handling, and Section 7.3.3 for troubleshooting errors.

7.3.1 Deduction in the ideal use of instruments

Error detection and diagnosis is demonstrated in this section at the analytical level of method performance and instrument use (Figure 6.3, Levels 2 & 3). Because the use of the MAS is rule-governed, the principle of measurement being based on the Beer-Lambert Law (A=εcL) (see Section 7.2.1), data are assessed as valid according to their compliance with the dictates of the Beer-Lambert Law. Whereas the Beer-Lambert Law applies specifically to molecular absorption spectrophotometry (MAS), analogous relations are described for the other forms of spectrophotometry, atomic absorption (AAS), flame emission (FES), molecular fluorescence (MFS), and nephelometry and turbidimetry (Holme, & Peck, 1998). There are inherent variations in all laboratory measurements, and significant random and systematic errors arise due to the limitations of methods and instruments, and technical errors by laboratory practitioners. Data can be assessed for compliance with the Beer-Lambert Law by direct inspection of instrument data windows, and by inspection of data represented graphically and statistically (Kringle, & Bogovich, 1999). Quality control (QC) and Quality Assurance (QA) programs are also needed 238 to assess performance over time, using control charts (Westgard, & Klee, 1999). Section 7.3.1 provides a brief explanation of the Beer-Lambert Law and the assumptions on which its valid application is based; Section 7.3.2 demonstrates the graphical and statistical representations of valid spectrophotometric data, as preliminary to the demonstration of error detection and quality monitoring in Section 7.3.3. The Beer-Lambert Law expressed in the equation, “A = εcL”, describes the relationship in molecular absorption spectrophotometry, between the absorbance “A” (or transmittance, %T) of EMR by a sample, a homogenous compound solution (molecules), at a specific wavelength (λ), and the molar concentration “c” of the sample solution (mol/L), placed in a light path of given pathlength “L” (e.g. 1cm) (Figure 7.4b) (Holme, & Peck, 1998, p. 53) (note that a molar concentration is a mole or mass in grams per molecular or atomic weight of substance, per litre). A constant directly proportional relationship exists between “A” and “c” such that a constant molar absorptivity coefficient (ε) (given as absorbance “A” of a molar solution [1 mol/L]), if known for the substance in question, can be used in calculations provided that pathlength, temperature, and wavelength are held constant (Holme, & Peck, 1998, p. 53). Alternatively, spectrophotometric data can be represented graphically, in terms of the relationship between light transmittance and concentration (also pathlength), by plotting the response data, “%T” or “A” as the dependent variable on the y-axis versus the dose or concentration, “c” as the independent variable on the x-axis on a Cartesian grid. In the case of transmittance, “%T”, the relationship demonstrated is inverse and exponential (Figure 7.7a). The linear relationship absorbance “A” versus “c” is demonstrated by transforming transmittance data logarithmically (A = -log T) (Figure 7.7b) (Holme, & Peck, 1998, pp. 49-50). Data are routinely transformed in this manner because linear data are more accurately interpreted than non-linear data. Because the pathlength is commonly held constant (e.g. 1cm), at least in manually operated instruments (Figure 7.3), the relationship “A” versus “c” only is of interest, and the rule is referred to simply as Beer’s Law. Compliance with Beer’s Law is assessed for absorbance data of standard samples placed in the light path, in the linear relationship between “A” and “c”, in graphical representations and statistics (discussed further in Section 7.3.2). A 239 preliminary assessment can be made of the validity of data by direct observation of the instruments’ behaviour as represented in “sensible” data in readout windows. At each step, the validity of the MAS performance is deduced in formal reasoning mode, by observing that experimental data (results) are compliant cases of the rule Beer’s Law (rule → case → result) (see Figures 4.11 & 4.12).

(a) Transmittance (b) Absorbance

T = transmittance It = intensity transmitted light Io =intensity incident light A = absorbance T= It A = -log T c = concentration Io L = pathlength

[c] in g/L or mol/L [c] in g/L or mol/L L 1 cm/10 mm L 1 cm/10 mm

Figure 7.7. The Beer-Lambert Law.

As with any theoretical principle however, the data are valid only if certain conditions are met. Data comply with Beer’s Law if standards and tests are matched for molar absorptivity coefficient (ε). If no standards are used, the correct “ε” must be given, in terms of specified experimental conditions, wavelength (λmax), pathlength and temperature. The limitations of Beer’s Law must also be considered because it fails at high concentrations at which molecular crowding distorts the response (Holme, & Peck, 1998, pp. 50-52). In the example of glucose estimation provided (Appendix B2.), the assumptions applying to Beer’s Law are hidden, and only the experimental conditions are given. Assumptions are addressed in the evaluation of data (e.g. Appendix B3.). A manual clinical chemistry experiment begins with a sample of whole blood that must be separated into red cells and plasma for the analysis of the constituent analyte of interest (e.g. Appendix B4.). Before conducting the experiment, the suitability of the sample must also be considered, to check for the pale straw normal colour of plasmas and determine if significant interferences are present (the colour of plasma which can cause significant spectral interference and false readings, is 240 addressed further in Section 7.3.3.1). In the two glucose methods (Appendix B2.), protein is removed from the plasma samples because it causes interference, then reagents and samples are reacted together in test tubes, and coloured reaction mixtures are produced that absorb light at specific wavelengths. The samples are then transferred to a cell or cuvette and presented to the MAS at POV 3 (Figures 7.4b & 7.8). After appropriate lamp (POV 1), and wavelength (POV 2) selection, absorbance readings are taken at POV 5. Data are then transformed graphically (test results being interpolated from the y to x-axis), and statistically for calculations and validation using the QC sample of expected value, assayed with the unknown test samples. Whereas the validity of data can be qualitatively assessed by observing the behaviour of the MAS instrument, in the data handling stages the validity of data are more precisely determined, graphically and statistically, as demonstrated in the next section.

A From Figure 7.4b [C] POV = point of variation 0.4 0.3 0.2 0.1 0 A POV 1 2 3 4 5 %T A vs %T 40 60 80 100 UV VIS λ L (1 cm)

%T [C]

SAMPLE INPUT Beer”s Law A = εcL

Assumptions re: λ [c] L T°C etc.

Figure 7.8. Rule-based MAS use.

7.3.2 Deduction in data handling and interpretation

Once data from instrument readings are accepted as “sensible” and recorded, they are transformed into graphs (if a standard curve is used, as is the case in the examples provided in Appendices B2-B4.) so that final results can be calculated. Compliance with Beers’ Law is deduced necessarily (rule → case → result) based on 241 the expected linear patterns in the graphs, and near perfect simple regression analysis, as explained in this section (Figures 6.3, Level 4, & 7.7b). The Beer- Lambert equation, “A = εcL” is the basis of MAS calculations which can be done in three ways, directly using the molar absorptivity coefficient “ε” if known; comparatively using a single standard of known value; or a series of standards is used providing a range of standards covering the expected sample value (Figure 7.9).

Calculations based on Beer’s Law A = εcL

1. If ε given, L = 1 cm, ‘A’ obtained from readout, then ‘c’ = A/εL

2. If one standard only, ATest = [Test] e.g. [Test ] = 0.250 x 5.0 = 3.6 mmol/L A Standard [Standard] 0.350

Assuming Beer’s Law is obeyed, and ε standard = ε test; ‘c’ is expressed as [ c ]

3. If a series of standards is used, plot graph ‘A’ vs ‘c’ (e.g. glucose range 3-15 mmol/L)

Figure 7.9. MAS calculations.

If “ε” is known, it can be used for calculations providing standard conditions such as wavelength, temperature and pathlength apply to the value given. The single standard method can only be used for well described methods known for compliance with Beer’s Law that is, linearity over certain ranges. When a series of standards is used, as is the case in the glucose methods illustrated (glucose standard range 3.0- 15.0 mmol/L [Appendix B3.]), compliance with Beer’s Law is demonstrated in two ways. Visual inspection of the graph directly reveals approximately how well the data comply with Beer’s Law. Compliance with Beer’s Law is deduced if a near perfect straight line is indicated, that passes through the origin (as demonstrated in Appendix B3.). If non-compliance with Beer’s Law is demonstrated, this is a clue that an error has occurred at some stage in the experiment. There can be no certainty about the cause of error once the measurement stage is completed, but the graphical representation gives a clue to the nature of the error. Statistical analysis using simple regression assists in the diagnosis of error (Kringle, & Bogovich, 1999; Myers, 1990; Weisberg, 1985). The graphic form of the Beer-Lambert equation, “A=εcL”, is isomorphic with

(same form) the algebraic polynomial of first degree “y = b0 + b1x” (where b0 is the y intercept on the x axis or x-y intersection, and b1 is the slope of the line, a change in 242

“y” for an equivalent change in “x”). Using this algebraic formation, the interpretation and interpolation of data can be performed quickly and with less error by calculators and computers (Kringle, & Bogovich, 1999). The information gained from visual inspection of data in a graph and simple regression analysis is crucial in drawing inferences about the nature of MAS errors (as will be illustrated in Section 7.3.3.2). The simple linear regression model applied to MAS data is based on the parameters or coefficients (b0 and b1) that characterize the line that best fits the data. The line of best fit is commonly determined by the least squares sum method (LSS), assuming that all data points are normally distributed (Figures 7.10a & 7.10b) (Myers, 1990; Weisberg, 1985).

y = b + b x 0 1 y y LSS = d12 + d22 + d32 + d42

d4 * * * d3 d5 * * A A * d1 * * * d2 Line of best fit *

[c] x [c] x

Figure 7.10a. Least Squares Sum (LSS). Figure 4.10b. LSS assumptions.

The least squares estimate is the “residual sum of squares” or the least sum of the errors squared for each data point from the fitted regression line, from which the coefficients, intercept (b0) and slope (b1) can be calculated and unknown sample values interpolated using a computer or calculator (Kringle, & Bogovich, 1999, p. 296; Weisberg, 1985, pp. 7-11). Further information about the nature of the relationship between “A” and “c” is given by the estimation of their co-variability using the correlation coefficient “rxy”, the degree of the relationship is given by the 2 2 2 coefficient of determination “R ” (note that R and r xy are identical for simple linear regression) (Burns, 1997, p. 197; Myers, 1990, p. 37; Weisberg, 1985, p. 19). MAS data will exhibit compliance with Beer’s Law if the simple regression analysis 2 indicates R ≈ 1 and b0 ≈ 0 (b1 ≈ 1 if units and scales are the same for x and y axes 243 which is not usually the case for MAS data) (e.g. regression analysis in Appendix B3.). Deviation from the expected slope, intercept and correlation parameters provides clues as to the presence and nature of errors. Regression analysis also aids in the interpretation of graphs, as is demonstrated Section 7.3.3.2.

7.3.3 Troubleshooting: Error detection and diagnosis

The knowledge worker in troubleshooting mode intercepts errors that constantly arise in analytical systems, chemical methods and other aspects of laboratory practice. This function is made possible by drawing on a large clinical chemistry knowledge base and extensive laboratory experience, and is supported by laboratory statistics and quality monitoring systems (Kringle, & Bogovich, 1999; Westgard, & Klee, 1999). Because of the vast amounts of information a laboratory scientist must manage each day, Expert Systems are used increasingly to assist scientists in many laboratory activities, including troubleshooting method and instrument malfunctions, quality monitoring and clinical interpretations (Sikaris, 2001). In the teaching situation, the manual performance of experiments introduces students to the rudiments of laboratory troubleshooting, error detection and diagnosis. These rudiments are demonstrated in this section guided by semiotic logic or sign action, in instrument use, data handling and quality monitoring. Evidence of troubleshooting activity is drawn from textbooks, course materials, and data collected from the teaching situation, observations of practical experiments in instrument printouts, and student practical reports, as explained in the design of semiotic analysis in Section 5.3.3.2. Four stages in troubleshooting are addressed. In the first section, troubleshooting is demonstrated in the validation of instrument use and method performance (Figure 6.3, Levels 2 & 3); in the second section, troubleshooting is demonstrated in the validation of data and results (Figure 6.3, Level 4); and in the third section, troubleshooting is demonstrated in quality monitoring (Figure 6.3, Level 5). Finally, the consequences of unchecked laboratory error are demonstrated in the clinical interpretation stage (Figure 6.3, Level 6). The transformations of data used as evidence for these purposes are explained with each section. It was explained in Section 7.3.1 that ideal instrument use in laboratory practice is deduced necessarily from data that comply with the requirements of 244

Beer’s Law that governs MAS experiments. In laboratory situations, in order to minimise error, experiments are designed to match Beer’s Law requirements, and protocols are well documented in manuals of procedure that also include troubleshooting sections (AACB, 1998b, 1999b; NATA, n. d.). It is possible that the more fixed and stable the procedure, the more likely Beer’s Law will become obscured, and deductive inference will be replaced with “auto-pilot”, which is not knowledge work. Shank (1998) assigns abduction as the “ground-state, default mode of cognition” (p. 841), but in laboratory settings, troubleshooting mode (abduction/hypothesis) as it is constructed in this chapter, is set in motion based on the deductive mode of inference. This is an example of the kind of circular argument that leads science philosophers to speak of hypothetico-deduction (Nickles, 1980; Popper, 1972/1979). Disputes about logic are avoided in this section, and the separate categories of logic are retained because they are useful for distinguishing between ideal use and error detection and diagnosis. Reasoning in troubleshooting mode requires recognition of symptoms and clues of errors in the environment, and inferences are drawn abductively by making reference to the rule governing the experimental situation, to diagnose the causes of error (see Section 4.3.3). The important consideration when logic is semiotic is that thinking is mediated by objects and representations (representation-object-interpretant relation, ROI [Section 4.3.2]). For example, symptoms and clues are tokens or actual evidence of error occurrences open to interpretation in the abductive mode of inference (see Figure 4.11). The symptom is evident to the senses in the present, whereas clues hint at past occurrences. Both must be interpreted as signs of unseen events in order for a diagnosis to be given. Symptoms and clues are thus the pre-interpretive aspects of signs (tokens) requiring abductive inference, but the nature of the sign to its object, iconic, indexical or symbolic, is not considered because it is too complex for the purposes of this chapter (see discussions of signs in Eco, 1976; Peirce, 1931-58; Sebeok, 1994). Troubleshooting in the next three sections demonstrates that knowledge work is symbolic analysis and symbolic analysis entails multi-literacies because different kinds of information are extracted from different forms of laboratory representations or inscriptions (Latour, 1990; Lemke, 2000).

245

7.3.3.1 Error detection and diagnosis in instrument use

Errors detected in the use of instruments can be attributed broadly to method performance and instrument use. Firstly, method or procedural errors arise due to sample errors, errors in reagent concentrations and standards, reagent omissions, extraction losses, poorly controlled reaction conditions such as pH, temperature and timing, and failure to adequately mix samples and reagents in reaction mixtures (the limitations of each method are discussed extensively in Burtis, & Ashwood [1999]). Secondly, errors in instrument use are demonstrated in the data readout window (POV 5), from which absorbance “A” data are recorded, after samples have been placed in the path of the light at POV 3 (Figures 7.4b & 7.8) (for discussion of deviations from Beer’s Law, see Holme, & Peck, 1998). There are many possible causes of aberrant “A” data and the purpose of knowledge work is to detect errors and diagnose their causes. This troubleshooting process is tracked using several sources of evidence. The instrument MAS (Figure 7.3) provides a primary source of data and the significant points of invariance (POV) identified in the expression line in Section 7.2.2.1, are used as the reference points at which signs of error can be recognised in the instrument while it is being used. Errors in MAS use may be made in lamp selection at POV 1, but choices are limited (deuterium or tungsten, or no choice if xenon arc lamp is used). Incorrect wavelength selection is a possible source of error (POV 2), but wavelength is predetermined for each method by the designer of the method. Errors in detection (POV 4) may occur due to voltage fluctuations and deterioration of phototubes, but this is not an every day source of error. Errors in readout (POV 5) are limited because there are two choices, “%T” and “A”, and “A” is specified in practical protocols, as is wavelength (e.g. Appendix B2.). The signs of error are first recognised at POV 5, and are commonly derived from errors at POV 3, the sample cell, its contents and placement, because this is the site of greatest operator intervention (Human, 1985). The discussion of troubleshooting in this section is, therefore, limited to error detection at POV 5 sample readout, in aberrant “A” readings, unexpectedly high, low, and unstable, as the symptoms of error, and the cause of the error is sought in the clues that reside in the sample cell and reaction mixture at (POV 3). It was explained in Section 5.3.3.2 that observation data of laboratory classes was sketchy due to the difficulties of demonstrating classes and recording 246 observations at the same time. The examples of error detection provided in this section are therefore limited to those for which there are course materials (e.g. Appendix B.), and instrument printouts (Appendix C.), or error scenarios are reconstructed hypothetically (or predicted) from Beer’s Law (Section 7.3.1). Only a few error scenarios are needed to demonstrate that troubleshooting requires knowledge of what might be expected in the use of the MAS, and recognition of symptoms of error in instrument behaviours. In knowledge work, symptoms are converted into signs by abduction of the rule Beer’s Law, in the diagnosis of errors for their probable causes (result → rule → case) (Figure 4.12). In order to demonstrate the troubleshooting process, the trail (semantic regress) is continued in the navigation of the semantic fragment in instrument selection (Figure 7.6). In knowledge work the analyst, when confronted with an aberrant “A” reading, high, low, or unstable, suspects an error and does not automatically assume the patient’s results are abnormal. Interpretation of aberrant “A” data requires checking the range of linearity of the method (by consulting the relevant reference or text) (e.g. Burtis, & Ashwood, 1999); the range of linearity of the instrument (by consulting the instrument manual) (e.g. Human, 1985); the range of standard concentrations used in the method (e.g. Appendix B2.); or the sample itself which may be causing interference in readings due to high colour or fat content (by inspecting the original sample as explained below). Methods are designed to minimize interferences from all sources and ensure that samples are in the optimum form for compliance with Beers’ Law. Many aspects of method performance and instrument use may result in an aberrant “A” reading, and the troubleshooting trail is made visible in the tracking of errors in the semantic fragment of MAS use (from Figure 7.6). In the context of measurement (Figure 7.11), the circumstance high “A” reading at POV 5 will constitute an aberrant result that sets off a trail of abductive reasoning (e.g. A = 1.800 would be a very high reading in the glucose experiment, in which the standard range indicated is 0.071-0.355 [Appendix B3.]). Whereas the problem could be due to the patient, the analyst must be sure, and therefore seeks validation of the data by looking for signs of error in the experimental situation. Several options might be considered, wrong cell material, reaction mixture turbidity, high plasma colour and procedural errors. The investigation proceeds in the logical 247 order of most likely causes of error and those that provide the easiest and cheapest solution. Wrong cell material is quickly eliminated by direct inspection of the cell being used because there are choices in sample cell composition - glass, plastic, silica or quartz crystal. Glass and plastic cells contribute minimally to readings in the Vis region (EMS), but their inappropriate use for UV readings requiring silica or quartz crystal, is indicated by “A” readings completely off the scale.

From Slide 7.6 Context 2 Measurement

Circ. 1 High “A” reading d1 wrong cell material d2 reaction turbidity d3 abnormal plasma d4 procedural error – sample unwashed Circ. 2 Low “A” reading d1 procedural error – extraction loss d2 pathlength error

Circ. 3 Unstable “A” reading d1 cell mishandled d2 sample unmixed Figure 7.11. Troubleshooting errors in MAS use.

Turbidity or cloudiness of reaction mixture can be directly observed, and in knowledge work it is noted that Beer’s Law applies only to clear homogenous solutions, so that turbidity invalidates the “A” reading. Turbidity is a sign that error has occurred, and its cause probably lies with faulty reagents and altered reaction conditions, pH changes or contamination. An audit of all the steps in the experimental procedure is required in such cases. The specimens being tested, usually sera or plasmas (whole blood minus red cells) exhibit variable colour, and are normally clear pale straw in colour (Figure 7.12).

(a) Plasma colours (b) Plasma spectral scans

1.0 Lipaemic Serum/plasma Haemolysis Icteric A Normal Red cells

Normal Haemolysed Icteric Lipaemic (pale straw) (pink-red) (orange) (cloudy-milky) λ nm 400 500 600 700nm

Figure 7.12. Plasma colours and spectra. 248

In certain circumstances, plasmas have high colour and this causes spectral interference, mainly false high “A” readings. For example, pink or red colour due to haemolysis of the sample (haemoglobin leaked from ruptured red cells into plasma), orange colour due to high bilirubin content in conditions of jaundice (and also carotene pigments), and cloudiness due to high blood fats (Figure 7.12a). Because spectral interference is excessive at certain wavelengths it is more problematic in some methods than others. The troubleshooter seeking the causes of high “A” readings therefore inspects the sample itself, and also the spectral scans of coloured plasmas in order to ensure that the wavelength used in the experiment is not one at which significant interference occurs (Figure 7.12b). In the case of haemolysis, the error can be corrected by recollecting the sample, but in the cases of jaundice and lipaemia, the problem lies with the patient (e.g. liver disease and high blood fats respectively, in the latter case, the patient may not have been fasting at the time of the blood test) (Young, & Bermes, 2001). If the sample appears normal and turbidity is not present in the reaction mixture observed directly, other types of procedural error such as method step omissions might be considered as causes of high “A” readings. An example is provided in the barbiturate analysis (Figure 7.13) (Appendices B6. & B7.).

(a) Extract unwashed (b) Extraction loss

observed observed expected expected

1.0 1.0

A A

∆A ∆A

260 λnm 260 λnm

Figure 7.13. Barbiturate experiment.

The drug is extracted from urine, purified, and scanned across a range of wavelengths, so that its type can be identified and it can also be quantified by comparison with the standard barbiturate specimen. A clue that a procedural error 249 has occurred is provided in the spectral scan of the test as compared with the standard scan (Appendices C2. & C3.). The divergence of the test spectral scan pattern from the expected pattern suggests that the sample is contaminated, and the most likely cause of contamination is the omission of the phosphate wash step for purifying barbiturate extracts (Figure 7.13a). The diagnosis of error in this case requires the analyst to track back over each step in the experimental procedure in order to pinpoint the most likely explanation. In the circumstance of aberrant low “A” readings (Figure 7.11), in the same barbiturates experiment; the knowledge worker draws inference about the nature of error from the clue residing in the graphical plot (Figure 7.13b) (Appendix, C4.).

Extraction loss is indicated because the ∆A260 reading is much lower than expected

(∆A260 represents the maximum difference between two different pH forms of the barbiturate standard used to compare standards with unknown tests [Appendix B7.]). Another source of low “A” data is derived from the analysts’ inattention to the Lambert aspect of the Beer-Lambert Law (A ∝ L) (Figure 7.7b), which requires the reaction mixture to be placed in the sample cell or cuvette in the light path of a specified pathlength (Holme, & Peck, 1998, p. 49) (Figures 7.14a & 7.14b).

1cm 1cm 1cm 1cm

0.300 0.300

3mL cuvette 3mL cuvette .3cm 1cm 1cm 1cm

0.100 0.040

1mL cuvette 3mL cuvette

Figure 7.14a. Pathlength error. Figure 7.14b. Lightpath error.

The signs of pathlength error are not easily detected because “sensible” “A” readings might be achieved despite pathlength errors. To detect a pathlength error it is necessary to inspect the placement of sample cell in the instrument, in order to ensure its correct alignment in the path of the light (Figure 7.14a), and also to ensure that there is sufficient sample in the path of the light (Figure 7.14b). In Figure 7.14a, 250 the reduction in pathlength has produced a reduced “A” reading, which can be directly demonstrated or predicted according to the equation “A=εcL”. In Figure 7.14b, there is no application of the Beer-Lambert Law. The circumstance of unstable “A” readings at POV 5 (Figure 7.11), is a sign or symptom of error requiring investigation at POV 3 to check for floaters in the reaction mixture, drips obstructing the light path, and inadequately mixed samples. The response of the instrument in such cases is impossible to ignore because no reliable reading can be recorded. Unmixed samples are invalid because Beer’s Law applies only to clear homogeneous solutions, and it may be too late to mix the samples because the reaction is modified over time. This trail of error detection and diagnosis can continue on ad infinitum. The few error scenarios provided are enough to demonstrate that the troubleshooter behaves like a diagnostician and a detective by picking up the signs of error as symptoms at POV 5 and by seeking out clues in the experimental situation in order to make a diagnosis, in the reaction mixture at POV 3, in the original specimen, and by following an audit trail back through the experimental procedure. Reading and interpretation of symbolic representations or laboratory inscriptions, in instrument printouts and data windows is central in troubleshooting errors in instrument use. If errors are missed in the real time situation there is a second opportunity to detect errors and reflect on their causes in the data handling and QC monitoring stages.

7.3.3.2 Error detection and diagnosis in data handling

Errors that occur in the practical situation resurface in graphical and statistical analysis of the absorbance (“A”) data produced in MAS experiments. This section demonstrates abductive reasoning to the clues of experimental errors hidden in graphic and statistical representations of “A” data. The clues of error hidden in graphs and statistics, interpreted in the light of the rule, Beer’s Law, can lead to a diagnosis of the nature of the error, although the precise cause of the error is difficult to determine after the fact. The data used as evidence of error detection and diagnosis is drawn from textbook information for ideal Beer’s Law compliance (e.g. Kringle, & Bogovich, 1999), and students’ practical reports that record error occurrences. The practical reports, as explained in Section 5.3.3.2, were analysed based on 251 demonstrators’ assessments of students’ practical reports. Demonstrators’ comments were collated, and sorted into categories of error. Only calculation errors, and students’ assessments of the validity of data and results based on compliance with Beer’s Law from the graphical and statistical representations of data (Figures 7.7 & 7.15), are of interest for the purpose of demonstrating data validation (Figure 6.3, Level 4). The demonstrators marked the practical reports in red pen, and in the photocopied student report, the comments are circled in red (Appendix B5.). The limitations of the data analysis process are discussed in Section 5.3.3.2, and the point is reiterated that no claims are made about students’ practical work in this section. The purpose is simply to draw out a few error scenarios purposively, in order to demonstrate how troubleshooting works ideally in symbolic analysis of graphs and statistics. Many practical reports were assessed in this way (approximately 900 in total) and common error scenarios were selected from 26 practical options (Appendix B1., and demonstrated below in Appendix D.). The glucose practical example used in Section 7.3.3.1 is used also for troubleshooting in data analysis in this section, because there are two glucose methods, and two sets of glucose data, graphs and statistical analyses which demonstrate the principle sources of error in data evaluations (Appendix B3.). The principal errors in graphical interpretation of data summarised in demonstrators’ comments, random error; misunderstood data; curvilinear data; loss of linearity; intercept error; and outlier, are simplified in terms of the graphical relations “A” versus “c” and statistical summaries, r2 and intercept (Appendix D.). The sample student practical report (Appendix B5.) does not contain all possible error occurrences, but the demonstrator’s comments (highlighted in red), point to the requirement for validation of data based on compliance with Beer’s Law, and also the need to match graphical interpretation of data with statistical analysis. Reiterating Section 7.3.2, MAS data are calculated using the Beer-Lambert equation, “A=εcL”, using “ε” or a single standard comparatively for well described methods, based on assumptions that “Beer’s Law is obeyed”. When a standard curve is used, compliance with Beer’s Law, or data validity is demonstrated in the patterns of data in graphical plots “A” versus “c”, assessed by visual inspection of the points on the line, and the assessment is supported more precisely using statistical analysis (Appendix B3.). Compliance with Beer’s Law is evaluated by examining the intercept (b0), slope (b1) and co-variability or random error about the regression line 252 given by r2 (Figure 7.15) (adapted from a method comparison study in Koch, & Peters, 1999, p. 324). Ideally, the graph “A” versus “c” is rectilinear and intersects the x-y axes at zero with all data points on the line, and regression analysis, r2 ≈ 1

(slope b1 ≈ 1.000 only if units and scales are matched for “x” and “y” variables) (e.g. Appendix B3.). If such compliance is not demonstrated, the level of acceptability of data is decided based on published recommendations, or by significance testing of hypotheses on slope, intercept and r2 (see Burns, 1997, p. 198, for examples of expected rxy values for different types of analysis) (see also Kringle, & Bogovich, 1999; Myers, 1990).

y (b) proportional error

(a) systematic error slope error b1 <1.0 b > 0; b = 1 0 1 * 2 Ideal r xy = 1.000 Intercept b0 = 0.0 ** slope b1 = 1.0 if axes the same * A * * * (c) random error r2 = 0.95 * xy

* *

[c] x

Figure 7.15. Compliance with Beer’s Law.

Clues as to the nature of errors are given by the patterns of non-compliant data. Figure 7.15a illustrates an intercept error indicating a systematic bias is present, caused possibly by the presence of an interferent, a blanking error in measurement, or an error in standardisation. Figure 7.15b, illustrates a change of slope indicating a proportional error in standardization, that is standardisation at the high end of the graph is in error. Figure 7.15c demonstrates co-variation r2 = 0.95, which is not acceptable for MAS data, and is indicative of random error, as occurs due to poor manual techniques such as inaccurate pipetting of sample and reagent volumes; and unstable reaction conditions caused by variations in pH and temperature and inaccurate timing. Drawing on the error scenarios documented in the analysis of practical reports (Appendix D.) knowledge work and symbolic analysis are demonstrated in 253 data validation by recognition of clues that reside in the graphical and statistical data representations. Taking up the trail in the semantic fragment (Figure 7.16), the first circumstance of data error is indicative of random error because the points are widely scattered about the line of best fit, denoting technical error has occurred (Figure 7.17a). Figure 7.17b implies misunderstanding about the relation between “A” and “c” altogether.

From Slide 7.6

Context Data validation

Circ. 1 Scattered data d. random error ( technical error)

Circ. 3 Curvilinear data d. error in chemical system

Circ. 4 Loss of linearity d1 outlier d2 deviation from Beer’s Law Circ. 5 Intercept error d1 blanking error

Circ. 6 Outlier d1 single renegade point Figure 7.16. Troubleshooting errors in data handling.

y y y 2 2 r = 0.967 r = 0.976 * ** * * r2 NA * A A A * * *

* * * * * *

[c] x [c] x [c] x (a) Random error (b) Misinterpreted data (c) Curvilinear data

2 y y y r = 0.993 r2 = 0.998 r2 = 0.985

b0 = 0.003 * r2 = 0.992 * * * * A A r2 = 1,000 A * r2 = 0.999 * b = 0.063 * 0 * * * * * * *

[c] x [c] x [c] x (d) Loss of linearity (e) Intercept error (f) Outlier

Figure 7.17. Graphical interpretation of data. 254

In the third error scenario, visual inspection of the data points suggests that the data might be curvilinear (Figure 7.17c). In this case the analyst drew a straight line arbitrarily, or a line of best fit, through a single point and zero, presumably because it was expected that the MAS data would be linear. Statistical analysis r2 = 0.967 will in this case support the conclusion that the data are invalid or at least require further interpretation (see for expected statistical analysis, Appendix B3.). Whereas random scatter of points as illustrated in Figure 7.17a suggest technical error has occurred, curvilinear data are more likely to be indicative of an aberrant chemical process as occurs due to poor temperature and pH control of the reaction, and too high concentration range for Beer’s law to be obeyed (see Holme, & Peck, 1998 & Human, 1985 for discussions of deviations from Beer’s Law). The symbolic analyst will conduct an audit of the experimental process in such cases in order to track the source of the problem (as described in Section 7.3.3.1). In the circumstance of data error indicating the loss of linearity at the top standard, two possible error scenarios are presented (Figure 7.17d). As the graph reveals there is loss of linearity after the fourth standard point, or alternatively the top standard is simply an “outlier” in a linear data set. Either way regression analysis for this data set r2 = 0.992 indicates that an error is present, and when the top standard is removed, r2 = 0.998. It is not valid however to extrapolate the graph above the fourth point, without knowledge of the method and the range of linearity expected for compliance with Beer’s Law. If the problem is loss of linearity and not an outlier, indicating a deviation from Beer’s Law and a change in the chemical system, the results falling in the extrapolated range will be falsely low, presenting the danger of misinterpretation in the clinical situation. In the circumstance of data error indicating an intercept error (Figure 7.17e), the intercept error was missed and a line was drawn arbitrarily through one standard point and zero, or the line selected was a line of best fit. The symbolic analyst would perceive the intercept error in the graphical pattern of the data because all points lie on the line except zero. The regression analysis r2 = 0.985 for the line through zero might be acceptable in some cases, but the new regression line omitting the zero, demonstrates improved correlation, r2 = 0.999, and the intercept error is highlighted, b0 = 0.063. The intercept error gives the symbolic analyst a clue that an error is present, and it might be due to incorrect referencing of the instrument or blanking. 255

Whether or not such data are accepted depends on the magnitude of the error and further QC assessment. In the circumstance of data error in which the presence of an “outlier” is indicated (Figure 7.16f) (An outlier is a point out of consensus with the other data points to an improbable extent based on theoretical expectations [Raggatt, 1997, p. 283]), the symbolic analyst can recognise the error by visual inspection of the data set, and the error becomes more prominent when data are graphically represented. The inclusion of the outlier by drawing a line of best fit results in an unacceptable correlation r2 = 0.993, and a probable misrepresentation of the data set. Because MAS glucose analysis has a theoretical basis, it might be deemed valid in this case to eliminate the outlier, and this results in the more probable regression line with r2 ≈ 1.000. Such an assessment however will have to be supported by further quality control (QC) assessments. Even if data appear to comply with the rules, based on graphical and statistical assessment, further assessments are needed in quality control (QC) and quality assurance (QA) to ensure that laboratory results are reliable over time. The complexity of symbolic analysis is compounded in QC monitoring with more statistical and graphical analysis, as is demonstrated in the next section.

7.3.3.3 Error detection and diagnosis in quality monitoring

The evaluation of the quality of a laboratory’s performance is far more complex than the relatively straightforward evaluation of MAS data. A range of analytical QC techniques are used to alert laboratory personnel to deterioration in analytical performance and the presence of error, to the nature of errors, whether random or systematic, and their possible causes. The evaluation of analytical quality (QC) must also be considered in the context of the broad plan of policies and procedures used by pathology laboratories to meet quality goals, termed Quality Assurance (QA). QA is in turn overseen by an overall management strategy termed Quality Management (QM), which aims to resolve the contradictory pressures to reduce costs and efficiency and at the same time improve quality (Rosenfeld, 1999; Weiss, & Ash, 1999; Westgard, & Klee, 1999). QA and QM are pragmatic issues and are discussed briefly as examples of value-adding in laboratory practice in Section 256

8.3. The main purpose of Section 7.3.3.3, given the complexity of QC, QA and QM, is to demonstrate that multi-literacies are used by symbolic analysts in quality monitoring in the validation of laboratory results (Figure 6.3, Level 5). On the one hand different kinds of information are extracted from charts that record QC measurement over time in order to ensure that results are reliable, and can be repeated from run to run, day to day, week to week, and month to month. On the other hand, unnecessary rejection of results must be avoided because it is costly, time consuming and inconvenient for clients (Westgard, & Klee, 1999). The aim of QC monitoring is thus to improve performance by increasing the probability of error detection, and at the same time reduce the probability of false rejection (p. 395). Two approaches to QC are considered in this section, the real-time assessment of accuracy using a QC sample in each experimental situation (note the presence of a QC sample in the Glucose Protocol, Appendix B2.); and charts for quality monitoring over time, so that trends, shifts, random and systematic errors can be picked up before they get out of hand. Because quality monitoring is difficult to achieve in the teaching context, the data used as evidence for this discussion are drawn from textbook information (e.g. Westgard, & Klee, 1999). The first approach to quality monitoring considered is the QC evaluation conducted in each batch, run or experiment with the main purpose of explaining how QC charts are constructed and interpreted, because single QC assessments are insufficient for quality monitoring. QC materials of known or expected values and similar matrices to unknown samples (e.g. serum or plasma), are analysed along with the test samples (Westgard, & Klee, 1999, p. 393). Two aspects of quality are assessed in the process, accuracy and precision. Accuracy is demonstrated in the closeness of a QC value to the “true” or expected value provided by the manufacturer, within limits. Accuracy assessment on one occasion however gives no indication of the reliability of results on repeated occasions. The precision of a method is determined by repeated measurements (Westgard, & Klee, 1999, p. 394). Acceptable QC limits as determined by the manufacturer, or reference laboratory are based on repeated measurements of QC samples using a reference method and highly controlled conditions. QC data are usually given in terms of a mean value (expected or “true” value) and standard deviation (s) for the repetitions (dispersion about the average value) (p. 394). The acceptable limits are set at ±2s based on probability 257 theory, that for data following a Gaussian or “normal” distribution (data are symmetrically dispersed about the mean or average value), approximately 95.5% of repetitions will fall within ±2s of the average value, and roughly 99.7% will fall within ±3s (p. 394). Outside these values, significant error must be considered. When a precision analysis is performed, standard deviation (s) provides an absolute value of the variability of an assay, but “s” is difficult to interpret in isolation. The placement of “s” in relation to the average value or mean provides a relative value, expressed as a percentage in the coefficient of variation (CV), for making easy comparisons across tests and methods. The lower the %CV, the better is the precision (Myers, 1990, p. 40). Figure 7.18 provides an example of glucose QC assays taken over a month, and calculation of accuracy expressed as % error of the expected value; and precision is given in absolute terms as “s”, and in relative terms as %CV. A QC assessment on each analytical occasion provides an inadequate assessment of quality that is monitored in charts over time.

QC Results glucose assay 1 month cycle

mmol/L 5.2 5.1 5.3 5.4 5.3 5.4 5.2 5.1 5.3 5.4 Day 1 2 3 4 5 6 7 8 9 10 mmol/L 5.2 5.1 5.0 5.1 5.2 5.1 5.2 5.3 5.1 5.0 Day 11 12 13 14 15 16 17 18 19 20 mmol/L 5.6 5.0 5.3 5.6 5.2 5.4 5.0. 5.2 5.6 5.1 Day 21 22 23 24 25 26 27 28 29 30

? = Σ x/n = 5.3 Days 1-10

2 s = √Σ (x - ?) /n-1 = 0.12 %CV = s/? x 100 = 2.3% %Error = (Observed - Expected)/Expected x 100 (5.2 – 5.3)/5.3 x 100 = - 1.9%

Figure 7.18. QC data and statistical summaries.

The second approach to quality monitoring considered in this section is the display of QC results on charts for monitoring performance over time in order to detect errors and deterioration in analytical systems. The Levey-Jennings chart for example is constructed by plotting QC data (e.g. Figure 7.18), observed values on the y-axis versus time on the x-axis, with the mean being placed at the centre of the y- 258 axis and control limits ±1s, ±2s and ±3s highlighted (Westgard, & Klee, 1999, p. 394). A symbolic analyst can by direct inspection of the chart, observe if an analytical system is in control, based on whether QC values fall within ±2s limits (Figure 7.19). There is a problem with this assessment however, because it increases the probability of false rejection (pfr), but to set control limits at ±3s, decreases the probability of error detection (ped) (Westgard, & Klee, 1999, p. 399). More complex procedures such as “Westgard multi-rules” are used to reduce the probability of false rejection and increase the probability or error detection, because unnecessary problem solving is time consuming, costly and inconvenient for clients (pp. 399- 401).

From Figure 7.18 QC (a) (b) (c) Result

• +3S control limit • •

• • • •

5.3 ? • • • • • • • • • • • • • • • • • • • -3S control limit • • • •

Time/days 1→10 Time/days 11→20 Time/days 21→30

Figure 7.19. Levey-Jennings Charts.

Other chart formations provide better quantitative assessments (e.g. the Cusum chart which is beyond the scope of this chapter) (p. 401). Additional chart formations are added in external QC programs or proficiency testing, which provide more extensive information about the nature of errors (also beyond the scope of this chapter). 259

QC control systems alert the symbolic analyst to the presence of errors and loss of analytical performance. They do not however identify the sources of errors, but provide clues indicating the nature of errors (Westgard, & Klee, 1999, p. 411). In the Levey-Jennings chart illustrated (Figure 7.19), two broad categories of error are indicated. Figure 7.19a indicates the analytical system for glucose analysis is in control over the first ten-day period because the QC data fall within the control limits ±2s, and are evenly distributed about the mean. Figure 7.19b indicates a systematic error because a shift in the mean can be discerned in the patterns of data. An inaccuracy problem is indicated possibly due to an error in standards (calibrators) or instrument calibration (Westgard, & Klee, 1999, p. 411). Note also the implied Gaussian distribution in each case, and that the data pattern in Figure 7.19b indicates good precision due to the narrow spread of data about the mean, despite the inaccuracy. Figure 7.19c indicates by the wide spread of data about the mean, that random error is present and the system is out of control. Such imprecision leads the symbolic analyst to consider the possible causes in technical factors, pipetting errors, inadequate mixing of samples and reagents, poor temperature control and reaction timing (p. 411). The quality of laboratory data is monitored in chart forms in the search for signs of error. Levey-Jennings charts are widely used in the pathology industry despite their limitations because they facilitate error detection and identification. Visual inspection of charts is thus added to the symbolic analysis of MAS data in graphs and statistics. Further sources of error in the clinical interpretation of results and their reporting (Figure 6.3, Level 6) are discussed in the next section.

7.3.3.4 The consequences of laboratory error

Once a test result is accepted as valid it is interpreted for its clinical significance, what it means for the patient and the physician (Figure 6.3, Level 6). In clinical interpretation, the situation becomes even more complex than it is in quality monitoring. This is because additional variables confound interpretations, including inherent biological variations, intra and inter-individual, daily, monthly and seasonal variations (Fraser, 2001, p. ix). Clinical interpretation begins with the comparison of test results with population “norms” or reference ranges established statistically, 260

“normality” being considered in terms of anatomical, physiological and biochemical knowledge (Fraser, 2001; Solberg, 1999). Errors in clinical interpretation can arise due to a number of factors associated with laboratory error before the clinician’s input is even considered. Because of the complexity of clinical interpretation, only a few examples are given in this section to demonstrate the kinds of error that lead to misinterpretation, after data and results have been accepted as valid. The following interpretation scenarios are associated in particular with enzyme analysis, and do not figure prominently in the data analysis provided (Appendix D.). Failure to report that a sample is of abnormally high colour, such as the red colour of haemolysed plasma (Figure 7.12), might in the case of potassium estimation for example, have fatal consequences for the patient in question (this is such a serious error that sample inspection, manual and automated, is a routine practice in clinical chemistry laboratories). Failure to match the units between QC expected value and standard value in question (e.g. QC value in mg/L versus standard value in mmol/L) can lead to unnecessary, time consuming and costly troubleshooting activities. Failure to match QC and reference range values with assay conditions, for example temperature and timing in enzyme assays, will lead to false results and misdiagnosis. Failure to check that different test results on individual patients are matched for a diagnosis such as hepatitis causes confusion and annoyance to clients. For this reason sets of results on individual patients are cross checked within and between the disciplines, and with the clinical notes supplied by the requesting physician, in order to ensure that they are diagnostically coherent, and do not provide conflicting diagnostic information. These are the kinds of errors which diagnostic Expert Systems are programmed to intercept (Sikaris, 2001). Such errors are tracked by quality monitoring and quality management systems such as the ISO9000 series, which are used for auditing work procedures in order to ensure standardisation, quality, efficiency and good service (ISO, n. d.; Westgard, & Klee, 1999). To take the analysis of the consequences of laboratory error any further would require a comprehensive on-site analysis of work in the pathology industry. This task is subject matter for another study (see Section 9.5). There is however, enough demonstration of knowledge work and symbolic analysis applying a semiotic framework in this chapter, to permit a provisional description of competence at operational, cultural and critical levels in the clinical chemistry discipline.

261

7.4 Knowledge work and symbolic analysis in clinical chemistry

This section provides a summary of the key components of knowledge work and symbolic analysis in clinical chemistry as facilitated by the application of semiotics, as summarised in the architectonic, structured aspects of scientific processes in Figure 4.18 (below the heavy line). Competence in the discipline clinical chemistry is thus placed on a theoretical footing. Four key aspects of knowledge work, three forms of logic, and symbolic analysis, are provided in response to the third research question: What modes of reasoning do knowledge workers use, and how do they add value in clinical chemistry laboratory practice? Logic has been considered for the purposes of this chapter in terms of induction, deduction and abduction/hypothesis. Induction has been demonstrated following the dividing up of a spectrophotometric instrument, the MAS, into its significant component elements (Figure 7.4), the components from which a catalogue of similar but different instruments was induced, arranged according to their theoretical functions (Figures 7.1 & 7.5). Whereas only one instrument was subjected to this treatment, the MAS, each instrument in the catalogue is amenable to the same structuralist treatment (division, classification and system). The significant points (POV) that permitted the classification of instruments are the same points at which the logic of MAS use was demonstrated. Deduction was explained as ideal rule- governed use of the MAS, in which the outcome of the experiment conformed to the theoretical expectations of Beer’s Law (Sections 7.3.1 & 7.3.2). Abduction or hypothesis was demonstrated as the logical response to an unexpected occurrence in the use of the MAS, by the recognition of the signs of error in symptoms and clues provided in the MAS readout window. This was followed up by navigating a semantic fragment of MAS theoretical and practical content relations, in order to diagnose the probable causes of error (Section 7.3.3.1). Such navigation requires unpacking of the relatively simple form, the Beer-Lambert equation, “A = εcL”, to reveal its multiple theoretical, technical, and pragmatic expression-content, content- content relations (Figures 7.2a, 7.2b & 7.6). In each of these logical explorations, it was demonstrated that knowledge work requires symbolic analysis. This is because each stage in the MAS use involves the manipulation and interpretation of laboratory 262 inscriptions, and the clues that reside in the tables of data, graphs, charts and statistics, guide the symbolic analyst towards error diagnosis in results validation. The symbolic analyst in working with the multiple forms of scientific representation (graphs, charts, equations, statistics, tables etc.) demonstrates the ability to manipulate them in two ways. On the one hand, different forms provide different information, and on the other hand, different forms provide similar information (as Saussure explains the difference in linguistic analysis, between linguist meaning and linguistic values, Section 4.2.1). The first example, the ability to extract different kinds of information from different representations, would be demonstrated in a more extensive analysis of quality monitoring than has been possible in this chapter (signfication). This first example is developed however in Section 8.4 with respect to laboratory test information. The second example, the ability to recognise the same information differently represented is exemplified in Section 7.3.3.2 in the analysis of MAS data (values). The theoretical relations between “A” and “c” represented in the Beer Lambert equation, “A = εcL” are also represented in verbal language as /rectilinear/; are equated with the polynomial algebraic form, “y = b0 + b1x”; are represented graphically in terms of “A” versus “c” on a Cartesian grid; and statistically in terms of the Least Squares Sum, and in 2 regression analysis in the form r = 1, intercept (b0) = 0, and slope (b1) (variable). These different forms should be in agreement about the experimental situation, and not produce conflicting information. The analysis of forms in this manner exemplifies the way multi-literacies are used in the sciences (Lemke, 2000). In the case of the MAS use, data analysis is relatively simple because linear relations are quite straightforward and are easily dealt with using calculators and computers. The importance of data analysis from first principles is emphasised however, in the case of non-linear data analysis, as occurs in immunoassay (Figure 7.1). This is because symbolic analysis in immunoassay is not rule-governed, and is open to wider interpretation. In curve fitting immunoassay data using polynomials of higher degree 2 3 4 (e.g. the fourth degree polynomial, “y = b0 + b1x + b2x + b3x + b4x ”), unexplained oscillations can appear in computerised curve fitting, and if they go unnoticed, erroneous results are produced. This is just one example of the need for human experts to monitor computers, to visually inspect data and not simply accept computerized analyses of data (Nix, 1994; Raggatt, 1997). 263

In the pathology industry, automation, robotics and informatics accomplish the wide range of activities required for the performance of thousands of tests each day, and this includes increasingly, troubleshooting instrument malfunctions, data and results validation, quality monitoring and clinical interpretation (Sikaris, 2001). Automation, robotics and informatics are thus replacing not only human movements with mechanical moving parts, but also human intellectual decision making functions (Rosenfeld, 1999, p. 490). Expert Systems however, can only respond to those problematic situations anticipated in the designs of their knowledge bases and inference engines. It is unlikely, as yet at least, that Expert Systems will be able to extrapolate and draw inferences about aberrant circumstances not accounted for in their data bases. Unexpected occurrences require human intervention (Chi et al., 1988; Gillies, 1996; Jackson, 1999). Given that automation and computerisation has transformed the nature of clinical chemistry laboratory work, it is important to target knowledge work and symbolic analysis in educating medical scientists. It is on the basis of recognition and interpretation of signs in the laboratory environment, for correct functions and malfunctions, and their ability to manipulate different forms of data in graphs, charts and statistics, that medical scientists can add value by the evaluation, optimisation and revision of methods. This is what makes competence operational, cultural and critical, “D” competence in the clinical chemistry discipline, reiterating Section 3.5.1.

7.5 Conclusion

Analysts of work in computerised environments redefine craftwork, commonly understood as the unity of hand and mind, in terms of intellectual knowledge work (Aronowitz, & DiFazio, 1994). As McGee (2002) explains, the new craftwork is knowledge work and because it takes place in the mind, its visibility requires improvement. In automated laboratories it is not the unity of hand and mind that is needed but the tacit integration of representations, objects and interpretations, and these three things come together in signs (Peirce, 1931-58; Polanyi, 1969). This chapter has applied semiotics to knowledge work and symbolic analysis in clinical chemistry laboratory practice. Semiotics permits a definition of knowledge work and symbolic analysis, which are interchangeable terms. The knowledge worker in clinical chemistry divides and classifies in the selection of analytical instruments; 264 navigates the knowledge base of clinical chemistry making logical rule-governed connections in the ideal performance of experiments; tacitly integrates representations and objects in instrument use and in troubleshooting errors; and manipulates different forms of data representations in the validation of results from experiments. The human expert (as opposed to the Expert System) has the ability to short circuit a vast knowledge base and network of rules, in order to make just a few local connections in pertinent semantic fragments, as the situation requires. This definition provides only a piece of the story however, because there are also pragmatic and social circumstances to consider, that are not necessarily the province of experts with “D” competence as so far described. It is also necessary to consider the extended forms of competencies needed when pragmatic circumstances such as space, staff and budgets confound rule-governed explanations. There is also the ability to read between the lines for ideological effects in the rhetorical manoeuvres used in communicating laboratory test information. It will be necessary to target this extended form of competency in the transdisciplinary courses needed for laboratory test evaluations in EBLM. This topic is addressed in Chapter 8.

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Chapter 8 The rhetoric of laboratory testing

8.1 Introduction

The semiotic framework applied to clinical chemistry in the scientific, structured analysis of the forms of scientific expression and logic conducted in Chapter 7, is expanded in this chapter to unstructured connotative analysis of scientific representations for their rhetorical or persuasive effects on scientific audiences. The pragmatics of laboratory practice is placed somewhere between the structured and unstructured levels of analysis. It must be emphasised however, that the structured scientific, pragmatic and rhetorical aspects of scientific practice cannot strictly be separated (Eco, 1976; Morris, 1971; Peirce, 1931-58; Toulmin, 1995). By considering pragmatics and rhetoric in laboratory practice, a response is given to the fourth research question: What range of competencies do knowledge workers apply in clinical chemistry, and what additional skills will add value in socially accountable laboratory medicine? The knowledge worker navigates semantic fragments of rule-governed expression-content relations based on theoretical and technological considerations, and modifies the rules to suit economic, political, social and other pragmatic circumstances. The knowledge worker is a symbolic analyst who can manipulate, and interpret laboratory inscriptions. In clinical chemistry these are principally given in the forms of graphs, charts and statistics. As the addresser in a communicative situation, the symbolic analyst can demonstrate that data and results are valid, and can present those data and results in such a manner, being rhetoric, as to persuade funding bodies and scientific audiences they are valid to use (Latour, 1987, 1990). As an addressee in a communicative situation, the symbolic analyst can discern inappropriate biases and vested interests masked in scientific information, beneath the rhetorical strategies or “framing effects” connoted in graphs, charts and statistics. When rhetoric is considered, it is not what but how things are represented, written or spoken that is important (Barthes, 1973/1964; Eco, 1976). The symbolic analyst, in presenting scientific information, will represent as many arguments and counter arguments relevant to the circumstances, in order to give audiences interpretive 266 choices; and not just those that suit the investigator, government, lobby group or anyone else with a vested interest in the outcome of a scientific investigation. This chapter develops insights into the way rhetoric is used in presentations of laboratory test information. Symbolic analysis of laboratory inscriptions for rhetorical framing effects is one way to approach social criticism in the sciences (Krips, McGuire, & Melia, 1995; Latour, 1987, 1990; Lenoir, 1998), and is complementary to approaches used in science philosophy (e.g. Feyerabend, 1975; Hesse, 1980), the sociology of science (e.g. Zukerman, 1988), and medical sociology (e.g. Petersen & Bunton, 1997), which are prominent sources of criticism of the natural, biological and medical sciences. Such criticism will be a requirement in Evidence-Based Laboratory Medicine (EBLM) because, in the evaluation of tests, social, legal, political and economic factors must be considered as well as scientific factors (see Section 3.2). This chapter is presented in three stages. Firstly, the range of competencies applied by knowledge workers in clinical chemistry laboratory practice is represented along a continuum from “d” to “D” competence, accounting for operational, cultural and critical competence in the clinical chemistry discipline, and criticism of the medical science field for the purposes of EBLM (following Gee, 1996; Lankshear, 2000) (see Section 3.5.1). Secondly, the shift from semantics to pragmatics in knowledge work is demonstrated by expanding on the MAS semantic fragment navigated in the selection and use of instruments in Sections 7.2.2.2 and 7.3.3.1 (Figure 7.6). Thirdly, symbolic analysis is extended to the “framing” of laboratory test information in graphs, charts and statistics in ways that persuade audiences of medical practitioners and health funding bodies that they are appropriate to use. Clinical chemistry course materials, in laboratory management (e.g. AACB, 1998b, 2001; AACB, n. d.; Rosenfeld, 1999; Weiss, & Ash, 1999); and in laboratory test evaluation (e.g. Shultz, 1999) are used as data sources (see Section 5.3.3.3).

8.2 The range of competencies applied to clinical chemistry Discourse

In this section an extended range of competencies is proposed for knowledge work in clinical chemistry, in addition to those detailed in Section 3.4.3, based on the competency standards for medical scientists set out by the profession (CBS-MS, 1993). In Section 7.4, operational, cultural and critical competence in clinical 267 chemistry laboratory practice was placed on a theoretical footing in terms of knowledge work, logic and symbolic analysis. Knowledge work and symbolic analysis must be further expanded beyond disciplinary requirements to include critical evaluations of laboratory tests for the purposes of EBLM. The range of competencies can be placed on a continuum from “d” to “D” competence. Operational competence (“d”) is required for engaging in the immediate requirements of the discipline; discipline specific or cultural competence is required for evaluating data and troubleshooting instruments and methods; and for evaluating, optimizing and revising analytical procedures (Figure 6.3, Levels 2-5). Critical competence is required for evaluating laboratory results for their clinical significance (Figure 6.3, Level 6), and laboratory tests, for their cost-effectiveness, clinical relevance, and appropriateness in given circumstances (“D”) (Figure 6.3, Level 1) (Muir-Gray, 1997; Price, 2001). The range of competencies is mapped out at three levels of criticism and reasoning, logic in rule-governed laboratory practices; and pragmatic discursive reasoning for internal managerial concerns; and for external crossdisciplinary concerns raised in the search for evidence that laboratory tests are valid to use. Each level of competence in laboratory practice entails rhetoric, the manipulation and interpretation of representations in symbolic analysis (Figure 8.1).

Competence Knowledge Action Logic “d” Architectonic Validation of data Critical interpretation 1 Mode 1 levels 2,3,4 Classification (induction)

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R R “D” Discursive Validation of tests Critical interpretation3 Pragmatic level 1 Pragmatics 2 (External factors) Mode 2 EBM Discursive reasoning

Figure 8.1. Levels of competence in clinical chemistry.

At the first level of competence, knowledge work (and symbolic analysis) is conducted at the architectonic structured level (Figure 4.18, below heavy line). Inductive, deductive and abductive logic apply in the applications of scientific 268 statements, rules, laws and principles, mathematically expressed in equations, in instrument selection, use and data validation (Sections 7.2.2.2, 7.3.3.1 & 7.3.3.2). With the second level of competence, explored in Section 8.3, knowledge workers add value by evaluation, optimisation and revision of methods made possible by competence at the first level. They can also think like managers by considering internal pragmatic factors that impact on method evaluations and decisions. In the selection of instruments, for example, parts and servicing, costs, simplicity, staff deployment, and the availability of space for operational efficiency and productivity, will be offset against the theoretical and technical performance characteristics of methods and instruments (Weiss, & Ash, 1999). There is a further logical possibility to consider from the semiotic perspective, that laboratory managers can reason to the hunch, and based on their perceptions of omens or signs of the future in the present, anticipate the next move in laboratory organizations (e.g. AACB, 2001; Isaacs, 1999) (see Figure 4.11). This level of critical interpretation, level 2, is internal, Discourse or discipline specific (Figure 6.3, Levels 2-6). At the third level of competence explored in Section 8.4, knowledge work is expanded, because even if architectonic, productivity and efficiency goals are met, this does not mean that laboratory tests are valid, cost-effective, appropriate, and clinically relevant for patients, communities and government funding bodies that partially fund laboratory activities (Farrance, 2000; Price, 2001). Testing for drugs, genetics, and tumour markers is particularly complex, laboratory test results might provide conflicting information, and decisions reached about them will in many cases have economic, political, legal, and social ramifications. EBLM is for this reason a transdisciplinary Mode 2 knowledge system. It requires panels of crossdisciplinary experts to decide which information provides the best evidence in different circumstances, and these panels will be influenced in making their decisions, by the way evidence is “framed” in graphs, charts, and statistics (Muir-Gray, 1997).

8.3 Pragmatics in laboratory testing

Pragmatics in laboratory practice is largely the province of laboratory managers, and no detailed treatment can be given in this section, because that would require a comprehensive analysis of work in the pathology industry, which is beyond the scope of this thesis. The purpose of this section is to demonstrate the way the 269 semantic space of theoretical and technical knowledge in instrument use (Figure 7.6) can be expanded to account for the non-scientific pragmatic concerns of laboratory managers, and other value-adding knowledge workers such as senior scientists. This brief discussion is based on theoretical expectations and textbook information as explained in Section 8.1. Reiterating on Section 4.3.5, semiotics provides a structure for knowledge in semantic fragments selected from a continuum of sign functions (expression-content relations), in a global semantic system of pertinent disciplinary knowledge (substance of the content) drawn from a universe of potential connections about a phenomenon in question (e.g. light). In navigating semantic fragments of knowledge, the knowledge worker maps out a pathway by making contextual and circumstantial selections to reduce interpretive choices to manageable proportions. Selections are motivated by primary rule-governed connections (denotations), directed by relatively stable scientific rules and procedures, as is illustrated in the MAS semantic fragment, representing the discourse unit <> (Figure 7.6). In the contexts of theory and measurement, the selection of instruments is based on internal scientific principles, and reliability and validity criteria (Figure 6.3, Levels 2 & 3) (Section 7.2.2.2). In the context of laboratory management however, there are other factors that override theory, because the best instrument may not be cost effective given pragmatic considerations such as space, staff and budgets (Figure 8.2).

From Figure 7.6 <> ≠ <> Context 1 ≠ Theory Plane of expression <> Plane of content ≠ Context 2 <> Measurement

Context 5 Context 4 Context 3 Evidence EBLM Politics Lab management    Circ.1 Circ.1 Economics Circ.1 Space  Tumour marker ‘X’  Circ.2 Legal Circ.2 Budget   Circ.3 Social Circ.3 Staff

To Figure 8.6

Figure 8.2. Expanded MAS semantic fragment. 270

In the selection of laboratory instruments a laboratory manager will make a trade-off between competing requirements for quality and efficiency of laboratory operations, considered within current economic policy (AACB, 1998b, 1999a, 2001; Rosenfeld, 1999; Weiss, & Ash, 1999). In clinical chemistry laboratories the physical and mathematical principles needed for decision-making about methods and instruments are subsumed within black box designs under layers of automation, robotics and computerised diagnostic functions (see Section 6.3.4). The MAS approach to chemical analysis becomes even more complex when the semantic fragment is expanded in the context of politics to incorporate economic, legal, and social issues (Figure 8.2). In the rhetoric of scientific communication, the way information is presented is important, not just for its scientific content, but also for its power to persuade and manipulate audiences, whether for noble or underhand purposes (Barthes, 1973/1964; Eco, 1976; Latour, 1987; Toulmin, 1995). Rhetoric and ideology in laboratory testing are explored in the rest of this chapter, in keeping with the main purpose to demonstrate symbolic analysis in social criticism, an additional value-adding competency required in EBLM.

8.4 Rhetoric in laboratory testing

There is more to the structure of scientific knowledge than the architectonic considerations of scientific statements (Figure 4.18, below heavy line). It is embedded in a complex set of social relations, practices, and belief systems (ideology), and is represented in highly specialised ways (rhetoric) (Krips et al., 1995; Latour, 1987, 1990; Lemke, 2000; Lenoir, 1998) (Figure 4.18, above heavy line) (see also Sections 4.2.2 & 4.2.3.3 for discussion of connotation). Eco (1976) refers to reasoning in this unstructured domain as “enthymematic”, meaning facts and beliefs are intertwined with pragmatic motivations, historical evidence and emotional considerations (p. 277) (reasoning can also be referred to as discursive for Mode 2 medical science knowledge [see Section 6.2.1]). In the sciences, representations of data in graphs, charts and statistics are rhetorical forms used to persuade other scientists to read data and results in certain ways (Krips et al., 1995; Latour, 1987, 1990; Lenoir, 1998). Given the vast stakes in medical interventions, drugs, diagnostic imaging and laboratory testing, an ability to read “between the lines” in the interpretation of laboratory inscriptions, provides a basis for social 271 criticism in EBLM. With the extended form of competence, “D”, critical of the Discourse, the medical scientist will be like the second “model reader” who can interpret not just what is said for its scientific content, but the way its is said to elicit certain interpretations over others (see Section 4.3.1). In EBLM, laboratory test evaluations will require symbolic analysis of data indicating technical performance and clinical utility, and also pragmatic information indicating the tests are appropriate and cost effective in the circumstances, and beneficial, not harmful to patients (Moyhihan, 1998; Muir-Gray, 1997; Price, 2001) (see Section 6.3.4.2 on EBLM). Two aspects of symbolic analysis of laboratory test information are discussed in this section to demonstrate the transition between structured scientific information and the connotations that emerge once the scientific data are viewed as a set from a particular world vision (Figure 4.18). The first aspect of symbolic analysis is relatively straightforward, entailing the switching between different forms of expression to extract different kinds of scientific information, as represented in graphs and charts (see Sections 7.3.3.2 & 7.3.3.3). In the example presented in this section, each representation, laboratory inscription, contributes different information about the cut-off values of tests as used for clinical diagnoses and other decisions about patients. In the process it is demonstrated that there are no right answers about cut-off levels for laboratory tests such as cholesterol and tumour markers (e.g. Prostate Specific Antigen [PSA]). The decisions reached represent a compromise or trade-off between the identification of false negative and false positive results, otherwise referred to as the sensitivity and specificity of tests. It depends on the circumstances, and also economic, legal and social factors, as to whether more false negatives than false positives will be tolerated in the selection of cut-off values for a test (Hobbs, 1999; Muir-Gray, 1997; Shultz, 1999). The PSA test is commonly used in the diagnosis, treatment, and monitoring of prostate cancer, and its usefulness is evaluated by comparing the PSA blood levels found in patients with prostate cancer with those levels found in patients with Benign Prostatic Hyperplasia (BNP) (Shultz, 1999). The information used in evaluating the PSA test is structured around the selection of cut-off values as presented in tabulated data sets that allow no room for interpretation; dot plots and graphical devices that are open to interpretation; the ROC curve (Receiver Operating Characteristic) which is commonly encountered in discussions about laboratory tests, despite its 272 limitations; and statistical representations such as “odd ratio” and “likelihood ratio” that determine the likelihood that disease will be present in a person with a positive PSA test (Shultz, 1999, p. 313). PSA data are modified in this section by substituting the PSA test with serum “X” used for the diagnosis of malignant tumour “Z”, which must be differentiated from benign condition “Y” (substituting for BNP). The focus of attention is thus placed on the different information provided by different data representations and judgments are avoided about the validity of the PSA test. As Shultz (1999) explains, cut-off values for laboratory tests are selected for use in disease prevention strategies, diagnosis, prognosis, and monitoring. Ideally a laboratory test will correctly predict the presence of a disease or condition for which the assay is deemed useful, so that the test is sensitive and gives no false negatives, and all positives for the disease are identified as such. Likewise the test will predict correctly the absence of a disease or condition, so that the test is specific, and gives no false positives that is, all cases without the disease are identified as such. The ideal laboratory test is 100% sensitive and 100% specific and doesn’t exist, so that decisions are made to tolerate false positives in favour of false negatives or vice versa depending on the test circumstances. If the cost of false positives is high because false positives lead to further testing, then the selection of cut-off values might favour more false negatives (Hobbs, 1999; Shultz, 1999). In applying specificity and sensitivity criteria to laboratory tests, several problems must be surmounted. Firstly, even if a laboratory test appears to be sensitive and specific, “spectrum bias” or sampling error raises questions about the probability and likelihood of particular individuals getting the disease even if they are registered as positive for a test (Shultz, 1999, p. 310). Secondly, the test itself is subject to “classification bias” without a gold standard against which it can be assessed, and frequently no such standard exist, which means the test is assessed merely in terms of its own performance (p. 311). Thirdly, even if these factors are addressed and the test is deemed clinically relevant, it must be affordable and appropriate to use in the local political, economic, and social circumstances (Muir- Gray, 1997). The decision to use a test as a valid medical intervention is a very complicated matter involving several complex manoeuvres and multiple scientific and pragmatic considerations. The acceptance of different rates of false positives and false negatives for laboratory tests such as PSA will vary from situation to situation. 273

In the case of test serum “X” used as an aid to diagnosis of tumour “Z”, the first data set (Figure 8.3) provides categorical statements about the sensitivity and specificity of serum “X” for the diagnosis of tumour “Z”. The statements are determined comparatively using data drawn from populations of people with tumour “Z” and benign condition “Y”. Two decision cut-off levels (4µg/L & 10µg/L) are compared for the prediction of malignant tumour “Z” versus non-malignant condition “Y”, or the absence of tumour “Z”.

Serum ‘X’ µg/L % Malignant tumour ‘Z’ % Benign condition ‘Y’

Cut-off 1. 4µg/L 81% TP (FN = 19%) 67% TN (FP = 33%)

Cut-off 2. 10µg/L 59% TP (FN = 41%) 89% TN (FP = 11%)

Sensitivity = TP Specificity = TN

↑ sensitivity →↓specificity

TP = true positives, TN = true negatives, FP = false positives = 100 - specificity, FN = false negatives = 100 - sensitivity Figure 8.3. Prediction of tumour ‘Z’ and benign condition ‘Y’ using serum ‘X’.

At cut-off value serum “X” = 4µg/L, the test is 81% sensitive (81% true positives identified), as compared with 67% specific (67% true negatives identified) leading to misdiagnosis of 33% of people with benign condition “Y” as false positives for malignant tumour “Z”. This false positive rate will be unacceptable for many people, creating unnecessary anxiety and expensive unnecessary medical treatments (Muir-Gray, 1997, p. 41). By shifting the cut-off to the higher level serum “X” = 10 µg/L, the specificity improves to 89%, the false positive rate being reduced to 11%. A new problem is introduced however, because there is a concomitant loss of sensitivity of serum “X” for correctly predicting malignancy, 59% true positives are identified and 41% of cases are misdiagnosed as not having malignant disease. If the malignant tumour “Z” is amenable to treatment and cure in the early stages, there will be consequences for the misdiagnosis, legal and social. On the other hand, false positive identification raises the possibility of mistreatment. In the case of tumour presence, there is the possibility of over-treatment if the disease is clinically indolent (disease is present but relatively inactive), which is a common occurrence in prostate 274 cancer (Shultz, 1999, p. 311). More information than that provided by Figure 8.3 is needed. The dot plot (Figure 8.4) provides more information in the topographical representation of data as compared to the typological representation of data in numbers in Figure 8.3. The dot plot gives an immediate visual comparison of each data set displayed in a frequency distribution at the two cut-off levels (4µg/L & 10µg/L) (e.g. 67% of dots below 4µg/L are true negatives with benign condition and 19% are false negatives for malignant disease).

Total = 27 cases Total =54 cases 20.0 ↑ 21 other cases z z z g/L

µ z z zz z z zz z z 10.0 z z Cut-off 2

Serum ‘X’ ‘X’ Serum zz z zz z zz zz z z zzz zz Cut-off 1 4.0 zzz zz zz zzz z z zz zz z z z z z zz z zz zz Benign condition ‘Y’ Malignant tumour ‘Z’

Figure 8.4. Dot plot of frequency distribution for serum ‘X’.

In current debates on visual literacy, topographical (iconic) representation is placed in opposition to typological (abstract symbolic) representation in symbols and numbers, in terms of the levels of meaning recovered from them (see Kress, & van Leeuwen, 1996; Lemke, 1998a, 1999, for discussion of typological [discrete variation and categorical distinctions] and topological representation [continuous co- variation]). Although an immediate qualitative impression is given of the numbers of positive and negative tumour “Z” observations (Figure 8.4), because only two cut- offs values are selected, the trade off between sensitivity and specificity that occurs each time a new cut-off is selected is obscured. A further change in the form of expression to a more topological plot (implied continuous variation which the dot plot is not) is given by the ROC curve (Figure 8.5). The ROC curve places the sensitivity or true positives (TP) predicted by a diagnostic test in direct correlation with the specificity (TN) defined in terms of 275 false positives (FP = 1 - TN). The y-axis represents the proportion of TP, the sensitivity, for which the goal is 100% represented on a scale 0.0 to 1.0. The x-axis represents the proportion of FP (1 – specificity) for which the goal is 0% on a scale 0.0 to 1.0. The ideal scenario is represented by the y-axis 0.0 to 1.0 with 0% FP and 100% TP. This situation is unlikely to be encountered, except perhaps in life and death situations that are not matters for debate, for example very high and very low blood potassium and glucose levels, or very high levels of drugs or poisons as might be found in comatose patients and corpses.

TP 1.0 — Sensitivity 0.9 — Cut-off 1 = 4 µg/L 81% > 0.8 — 0.7 — 59% 0.6 — >Cut-off 2 = 10 µg/L 0.5 —

— — — — |||||| ||| | 0.1 0.5 1.0 11% 33% FP (1 - specificity)

Figure 8.5. ROC curve.

The third diagonal axis (dotted line) represents no discrimination between cut-off values, and an even chance of correctly predicting or falsely predicting the presence of tumour “Z”. The ROC curve indicated by the wavy line (each turn on the line represents a new cut-off value and a new true and false positive rate), lies between the ideal and the useless situation, and is partly based on scientific factors and partly based on an arbitrary decision, once pragmatic factors are considered. The ROC curve permits direct observation of the sensitivity and specificity of tests at all cut-off levels, and as the sensitivity of an assay improves it loses specificity and more false positives are identified. A test can thus be as sensitive or specific as required simply by changing the cut-off level (Shultz, 1999, p. 314). The ROC curve has limitations and cannot be used in isolation because it tells nothing of the many pragmatic factors considered in making a cut-off selection. Without full details of a test’s evaluation, classification and sampling biases are 276 masked; disease prevalence is not accounted for, which means that there is no indication of the probability or likelihood, given a positive test, that the disease will be present in individual patients, because individuals testing positive for a test might not be represented in the population studied. The assessment of a test’s predictive value requires additional statistical tools of analysis to determine the probability (odds ratio) and likelihood (likelihood ratio) based on disease prevalence studies in populations, that the disease will be present given a positive test (Shultz, 1999, p. 314) (see also Muir-Gray, 1997, p. 117). As in the analytical situations demonstrated in Sections 7.3.3.2 and 7.3.3.3, graphical representations of test data are supported by statistical analyses in making decisions. According to Shultz (1999), the selection of cut-off values for laboratory tests is so complex that attention is being turned to multivariate analyses of population reference ranges and decision trees embedded in “neural networks”. Thus, in addition to the cascade of representations of laboratory test information demonstrated in this section, Expert Systems and Artificial Intelligence (AI) are considered for making decisions about the diagnostic values of laboratory tests (Schultz, 1999, p. 318) (see also Muir-Gray, 1997, p. 94). Expert Systems and AI are limited in what they can do, as discussed in Section 7.4 (Chi, Glaser, & Farr, 1988; Gillies, 1996). From the semiotic perspective, the skills required of a human expert in the context of evidence entail the ability to navigate a vast data base of knowledge and operational rules in order to make theoretical and pragmatic decisions; the ability to short-circuit the unwieldy semantic system by navigating locally in pertinent fragments (see Sections 4.3.5.3 & 7.2.2.2); the ability to interpret information in graphs, charts and statistics as demonstrated in Sections 7.3.3.2 and 7.3.3.3, and in this section. Another aspect of symbolic analysis to consider in evaluating laboratory tests is the “recognition” of values and world visions implied in that which is represented, written and spoken; and that representations mask alternative equally valid interpretations. This aspect of symbolic analysis is conducted in the plane of connotations (Figure 4.18, above heavy line), and there is an infinite variety of connotations that bring alternative perspectives into focus, as was demonstrated in the analysis of the system of transportation (Section 4.3.5.3). Connotative analysis can be applied to discussions about the validity of laboratory tests. In the forms of expression of the primary denotations about tests, scientific facts are offset with pragmatic circumstances, legal, political, economic 277 and social, and they also “speak” the ideals and values of medical science Discourse itself (Figure 8.3) (see Sections 4.2.2, 4.2.3.1, & 6.3.4). A laboratory test cut-off value can be used as the point of departure for this kind of analysis. In the context of evidence within the frame of orthodox Western medicine (substance of the content) (Figure 4.4), alternative viewpoints are brought into focus when the false positive and false negative consequences of different cut-off levels for tests are weighed up in different circumstances. For example, in the evaluation of serum “X” for the correct prediction of malignant tumour “Z”, the different cut-offs chosen will be based on evaluative judgments as much as scientific statements, and the connotations “good” (c+) and “bad” (c-) can be assigned in certain situations (Figure 8.6).

From Figure 8.2 Context 5 Dx = diagnosis Evidence EBLM Rx = treatment d. = denotation (c+) & (c-) = connotations Circ 1. Cut-off serum ‘X’ = 4 µg/Ld. sensitive test Circ. = circumstance  Circ. 1 active tumour- surgical removal (c+) “good” result  Circ. 2 indolent tumour - surgical removal (c-) “bad” result Circ. 2. Cut-off serum ‘X’ = 10 µg/L d. specific test  Circ. 1 active tumour - no Dx & no Rx (c-) “bad” result  Circ. 2 indolent tumour - no Dx & no Rx (c+) “good” result

Figure 8.6. Connotations of laboratory test cut-offs.

The first circumstance of the low cut-off for serum “X” = 4 µg/L denotes a fairly sensitive test producing few false negatives but a high proportion of false positives is encountered. If an active tumour is present, a positive test may result in surgical removal of the tumour. If successful a “good” result is connoted because it permitted early treatment. This viewpoint is biased however, towards surgical intervention and masks counter opinions that surgery is difficult in cases such as prostate cancer. Judgments about survival in cancer cases are flawed if they fail to account for “lead time” bias, or lack of knowledge about how the long the disease has been present (Muir-Gray, 1997, p. 50). If the tumour is not active but indolent, no treatment is indicated and if inappropriate treatment is given, the low cut-off level connotes a “bad” result. In the second circumstance of the high cut-off value, serum “X” = 10 µg/L denotes a less sensitive but more specific test (fewer true positives and also fewer false positives). On the one hand, failure to treat an active tumour 278 missed due to the high cut-off level connotes a “bad” result. On the other hand, failing to treat the indolent tumour connotes a “good” result, because no treatment is recommended. There are no right decisions in such cases. It is a matter for debate. If a condition is considered to be life threatening if not diagnosed and treated, the trade off might favour increased sensitivity (increased true positive identification), also causing decreased specificity (increased false positive identification), and varies from case to case. Knowledge workers in clinical chemistry are symbolic analysts with operational, cultural, and critical, “D”, competence, geared for socially accountable EBLM. The symbolic analyst can extract different scientific information from different forms or representations, in tables of data, graphs, chart and statistics. For the purposes of transdisciplinary Mode 2 knowledge systems such as EBLM, value is added by symbolic analysts who bring alternative viewpoints to the surface in navigating the semantic space of laboratory test information. Symbolic analysts can discern framing effects in scientific representations, unwarranted ideological biases and vested interests that distort and manipulate interpretive choices.

8.5 Conclusion

This chapter demonstrates that laboratory test evaluation is no simple matter, and is based on symbolic analysis of graphs, charts and statistics. It might be argued that the complexity of EBLM is beyond the scope of an undergraduate medical science curriculum. This will not be a valid argument however, if the viewpoint is taken that the cultivation of so-called liberal as well as vocational skills is a requirement of a university education (see Section 3.3). This requirement is particularly prominent considering the transition to transdisciplinary forms of knowledge in universities, also referred to as Mode 2 knowledge (see Section 3.2); and that medical scientists are being displaced by automation, robotics and informatics, but opportunities are looming in EBLM. The new skills needed by knowledge workers and symbolic analysts in EBLM include the ability to recognise unwarranted biases and vested interests connoted in laboratory test information. Semiotics in its principal role of social criticism is useful for scientists because it brings rhetoric and ideology within the same abstract framework as scientific knowledge. 279

Chapter 9 “Knowledge work” in the medical sciences

9.1 Recapitulation of research problem

Semiotics, a system for analysing signs and representations, has provided a powerful theoretical framework in this research for exploring competency requirements and contemporary work practices in the medical sciences. This investigation was motivated by perceptions in the academic, university sector, that otherwise capable clinical chemistry students were failing to integrate pre-requisite theories (e.g. physics, chemistry, biology, mathematics, and statistics) in their practical activities; were failing to consider the relations between theories in analytical measurement and the selection of instruments; were failing to detect, diagnose and troubleshoot errors; were failing to reflect on the quality of their results; and were failing to adequately handle and interpret different forms of scientific representation, particularly graphs, charts and statistics. Because of limitations placed on data collection techniques, the causes of students’ errors remain unanswered, awaiting further investigation. Available data sources, professional journals, clinical chemistry course materials, observations of laboratory activities, instrument printouts and practical reports have, however, provided evidence supporting the proposition that semiotics can be used to construct a theoretical ideal of knowledge work in contemporary clinical chemistry laboratories; which can in turn be used to address the problems observed in laboratory classes. In order to meet this challenge, in addition to semiotic theory, other problems posed by researchers of professional work and higher education were considered. Firstly, the reengineering of pathology laboratories due to advances in automation, robotics and informatics, has caused a shift in the emphasis of laboratory work along a continuum from manual to intellectual knowledge work. It was noted that knowledge work in computerised laboratories entails symbolic analysis, because the emphasis of work is placed on manipulation and interpretation of abstract data and symbols produced by instruments, and analysed using computers (Aronowitz, & DiFazio, 1994; Barley, & Orr, 1997; Drucker, 1993; Lankshear, 2000; Reich, 1992). 280

Displacement of workers was also noted as a consequence of automation and computerisation. Secondly, a shift in the mode of knowledge production in the sciences from specialist disciplinary to collaborative transdisciplinary forms of knowledge has blurred the boundaries between the pure and applied sciences, and between work and academia (Gibbons, Limoges, Nowotny, Schwartzman, Scott, & Trow, 1994). As a consequence, alternative avenues of employment are emerging along with new skills requirements in the medical sciences, particularly for the search for evidence that laboratory tests are valid to use, as conducted in Evidence- Based Laboratory Medicine (EBLM). Thirdly, the new skills and displacement issues were considered in terms of the competencies, literacies and forms of thinking needed for work in the new knowledge environments. Criticism in disciplines and of Discourses were considered, for the benefit of communities and workers, as well as employers (Gerber, & Lankshear, 2000; Symes, & McIntyre, 2000). It was concluded that although considerations of professional expertise (and Expert Systems) from cognitive perspectives are important (e.g. Chi, Glaser, & Farr, 1988), it is necessary to consider the way learning in technical professions takes place in the nexus of activities, tools, and culture (Brown, Collins, & Duguid, 1989). Such activities are mediated by representations, abstractions or academic symbolic descriptions (Laurillard, 2002). It was also concluded that higher education in the sciences requires greater attention being given to symbolic analysis, and the multi- literacies used in manipulating forms of data in graphs, charts and statistics, in order to extract different kinds of information about laboratory results, tests and patients (Lemke, 2000). Taking a socio-cultural perspective, it was considered that professional learning requires a process of enculturation into the ways of a profession, for its specialised ways of viewing the world, its representation, and the use of technologies, tools or instruments. It was also considered that there are constraints imposed on scientists in laboratory environments that might limit their knowledge work experience. It was proposed that semiotics provides a tool of cultural analysis applicable to the structure of medical science Discourse, knowledge base and context; the logic of practice; the rhetoric used in communicating scientific information, and the values that underpin interpretations. Structure, logic and rhetoric come together in semiotics in signs and representations. In conclusion, taking the semiotic ideal of knowledge work, knowledge workers in the medical sciences help keep the pathology industry competitive and 281 viable by competent work performances, innovations and method improvements. They also provide socially critical perspectives in EBLM, and at the same time their horizons and options as lifelong learners are expanded. This chapter summarises the findings of semiotic analyses of clinical chemistry demonstrating its application to knowledge work and symbolic analysis in the medical sciences; and to the structure of the knowledge base and the contextual constraints imposed on the knowledge work experience. The contribution this research makes to higher education and knowledge is also addressed, plus further research, and implications and recommendations for medical science education.

9.2 Summary of findings of semiotic analysis

The three aims of this research as set down in Section 1.4 have been met. Firstly, a meaning has been found for knowledge work and symbolic analysis in the “new knowledge” clinical chemistry laboratory environment with the aid of semiotic theory. Secondly, a semiotic framework has been derived from a wide range of semiotic theories that is applicable to clinical chemistry knowledge and practice in automated computerised laboratories, and EBLM (Chapter 4). Thirdly, the effectiveness of the semiotic framework has been demonstrated by its application to clinical chemistry transdisciplinary (Mode 2) knowledge (Chapter 6), and knowledge work and symbolic analysis in laboratory practice (Chapters 7 & 8). The picture of knowledge work and symbolic analysis was constructed by exploring literature from the pathology industry professions, higher education, and semiotic theory. The review of changes in the pathology industry presented in Chapter 2 indicated medical scientists’ roles and conditions of work were changing due to automation, robotics and informatics. These changes put medical scientists in competition with diagnostic Expert Systems, in addition to automation and robots, thereby creating the potential for de-skilling and displacement. It was concluded that knowledge work in automated laboratories requires more skills in symbolic analysis, because more complex systems of analysis and data handling techniques have been enabled by technologies and computers. The problem was also noted that there are fewer highly skilled work positions but new opportunities will arise in EBLM. It was concluded that knowledge work and symbolic analysis would have to be targeted 282 more purposively in medical science education, and be described more fully, and that additional skills will be needed for participation in socially accountable EBLM. A review of research literature into higher education was conducted in Chapter 3 with the purpose of finding a theoretical basis for knowledge work and symbolic analysis applicable to clinical chemistry. Several pertinent issues were raised in the process, which gave insights into knowledge work, competency and the problem of displacement. A shift in the mode of scientific knowledge production was noted by some researchers, so that disciplinary knowledge (Mode 1) has become incorporated into more collaborative transdisciplinary forms of knowledge (Mode 2). It was explained that this shift has been forced as a response to economic globalisation, advances in computing, instrument and communications technologies, and the inability of single disciplines to solve complex real world problems (Gibbons et al., 1994) (Section 3.2). This shift, which is exemplified by the biotechnology industry and EBLM, is important for medical science education because it blurs the boundaries between disciplines, and the pure and applied sciences, thus creating new avenues of employment and research opportunities for displaced medical scientists. It was also noted that because the boundaries are blurring between workplaces and universities as primary sites of learning, there is the potential in workplaces for the formulation of Work-Based Learning (WBL) degrees (Boud, & Solomon, 2001; Gibbons et al., 1994; Symes, & McIntyre, 2000). This challenges the way medical science courses are conceived. The issue of competency for medical scientists was expanded beyond the original formulation of competency, namely Competency-Based Standards for Medical Scientists (CBS-MS, 1993), to account for skills in symbolic analysis needed in computerised knowledge environments, and for socially accountable EBLM. Taking a socio-cultural perspective, it was concluded that there are operational, cultural and critical dimensions of literacy and competence to consider (Section 3.5.1). Critical competencies in the clinical chemistry discipline are needed to ensure that data and results and their clinical interpretations are valid, and that methods are subjected to ongoing evaluation, innovation and improvements (Koch, & Peters, 1999). It was also considered that skills in social criticism would enhance contributions to socially accountable EBLM, and at the same time prevent students from being colonised or indoctrinated by medical science Discourse (Gee, 1996; Lankshear, 2000). Competence from a socio-cultural perspective was expanded in terms of the ability to participate in a scientific community, using its 283 specialised ways of behaving, thinking, valuing and believing, and of using specialised languages and tools (Gee, 1996; Lankshear, 2000). In other words, knowledge workers in scientific cultures have the ability to manipulate and interpret multiple forms of scientific representations, verbal language, tables of data, graphs, charts, diagrams and statistics (Lemke, 2000). These multi-literacies also entail the ability to interrelate different forms of knowledge; to integrate representations or abstract symbolic descriptions and theories; and to integrate theories and representations in practice situations (Laurillard, 2002) (Section 3.5.2). The pragmatic basis of the claim that theories, ideas, objects, and representations, are integrated in practice, was explained in terms of the sign triad of Peirce (sign-object-interpretant relation) (Section 4.3.2). Knowledge work in this light entails the tacit integration (following Polanyi, 1969) of the objects in the environment, the abstract symbolic descriptions emerging from activities with objects, and the theories and concepts involved. From pragmatic sign theory, the proposition emerged that semiotics provides a suitable framework for integrating the structure of clinical chemistry knowledge with the logic of laboratory practice. It was explained in Chapter 4 that semiotics, by drawing on structural linguistics, science philosophy, and sociology, provides mechanisms that give structure to Discourses (Section 4.2), and insights into laboratory experience (Section 4.2.4.3). Semiotics explains knowledge work and symbolic analysis in terms of logic incorporating the manipulation of representations (Section 4.3). Four aspects of semiotic theory were considered as applicable to the medical sciences: syntactics, the relations between signs, roughly equated with structure; semantics, the cultural meanings attributed to signs and hence the relations between signs and objects, roughly equated with logic; and pragmatics, the relations between signs and interpreters, also associated with rhetoric and ideology, the way things are communicated, and the values that underpin interpretations. A pragmatic function of semiotics is therefore social criticism. Because semiotics is primarily a theory of signs and representation, thinking is explained as sign action or semiosis, which involves a chain of connections from sign to sign in an infinite progression (Section 4.3.2). Informal logic is explained as sign action, the integration of pre-interpretive objects perceived in the environment, such as symptoms, tracks, and clues, and their interpreting representations, interpreted as signs to explain unexpected events. The fourth aspect of semiotics, socio-semiotics, provides another avenue for social 284 criticism by incorporating social science and humanities perspectives, particularly those of sociology. Socio-semiotic analyses examine the articulation between ideology and material forms, using structuralist principles to guide the analysis at the denotative level. By superimposing connotations on the denoted first order meanings given to cultural systems, insights are gained into consumer experience. The semiotic framework places knowledge systems in relations with each other, in expression-content relations (forms and substances) (Figure 4.4). Fragments of knowledge from specific fields and disciplines are drawn from this structure and are subjected to analysis at scientific denotative and non-scientific connotative levels (Figure 4.18). Firstly, at the structured scientific level, specific forms and contents of knowledge systems are analysed in terms of relations, in the plane of expression and/or the plane of content. At this level, objects are described and classified, and in some cases patterns emerge permitting propositions to be made about generalised systems. At the second level such propositions are tested by scientific experiments leading to their verification or rejection. At the third level, through the mechanism of connotation, the way things are represented, designed, written, or spoken, being rhetoric, gives insights into the values or ideology behind the knowledge system. From the semiotic perspective, knowledge work and symbolic analysis are interrelated terms. Knowledge workers are symbolic analysts who can deal with relations; the interrelations between theories (content-content relations); between representations (expression-expression relations); between theories and representations (expression-content relations). They have the ability to apply logic in rule-governed situations, integrating representations, ideas and objects, in the classification of objects, their use, and in error detection and diagnosis (in a manner similar to crime detection). In knowledge work, decisions are made to override theory in certain circumstances; and there is also discrimination among values in making interpretations; and recognition of the way rhetorical devices are used in communicative situations to persuade others which interpretations to make. With the purpose of demonstrating the applicability of this construction of knowledge work in the medical sciences, three semiotic analyses were designed in Chapter 5, by drawing on data sources collected from a clinical chemistry teaching situation. The three analyses were guided by four research questions, pertaining to the structure of clinical chemistry Discourse; the constraints imposed on the knowledge work experience by the materiality of the laboratory environment, and the 285 values of the Discourse; the logic that applies in laboratory practice; and the additional skills needed for socially accountable laboratory medicine (Section 1.4). There were two components to the analysis in Chapter 6, addressing the first two research questions with respect to the structure of clinical chemistry knowledge base, and the laboratory environment. In order to answer the first question: What structure applies to knowledge work in automated computerised laboratories and EBLM?, clinical chemistry was located within medical science Discourse under the cultural sign Health, and placed in conceptual opposition to other approaches to Health (Figure 6.1). This process was assisted by other approaches, the structure of scientific disciplines (following Schwab, 1962, 1964b), which is built upon their conceptual structures interrelating theories; and their pathways to verification or scientific method. Because clinical chemistry requires verification of the applications of scientific theories with the aid of graphs, charts and statistics, a systematic basis was provided in terms of its validation procedures, chemical analysis systems, methods, calculations, quality monitoring, and clinical interpretations (Figures 6.2 & 6.3, Levels 2-5). The structure of clinical medicine as defined by Foucault’s archaeological method (Foucault, 1966, 1970, 1969/1972) provided insights into the structure of clinical chemistry transdisciplinary knowledge, incorporating social, political, legal, institutional, technological, and other pragmatic factors in addition to scientific knowledge. Such considerations are fundamental in the evaluation of laboratory tests for their clinical relevance, cost-effectiveness, and appropriateness in EBLM (Level 1 validity). Because the structure of clinical chemistry knowledge is impossible to represent, it was proposed that pertinent fragments of knowledge would be selected when considering its use, but addressed to this core set of validation procedures in order to demonstrate logic and pragmatics, rhetoric and ideology in laboratory practice in Chapters 7 and 8. In order to address the second research question: What contextual factors constrain knowledge work in automated computerised laboratories? The teaching laboratory was subjected to the structuralist method. It was ordered around the variant, “spatial arrangements”, in order facilitate its description and comparison with industry laboratories. This process provided insights into the reengineering process enabled by advanced automation, robotics, and informatics, which is modifying the way work is conducted (Sections 6.3.1 & 6.3.2). Connotative analysis of laboratory instruments was superimposed upon this descriptive, comparative 286 analysis by emulating the strategies used by cultural theorists who explore the way material forms express symbolic cultural values as well as functional use values. In the process socio-ideological insights were gained into laboratory experience, and implications for the education sector were considered with respect to the potential for de-skilling and displacement of medical scientists in computerised laboratory environments (Section 6.3.4). It was proposed that deliberate attention be placed on symbolic analysis for the cultivation of knowledge workers who can interrogate fully the activities of computers, particularly Expert Systems that are increasingly performing the diagnostic and interpretive functions of medical scientists. It was also noted that in order to participate in EBLM, skills are required in complex symbolic analysis of information in laboratory test databases, and systematic reviews of existing research. It was also suggested that socially critical perspectives of the kind commonly associated with medical sociology are needed to bring inappropriate biases and vested interests to the surface. In order to explain the logic of laboratory practice, a semiotic response was given to the third research question in Chapter 7: What modes of reasoning do knowledge workers apply, and how do they add value in clinical chemistry laboratory practice? Three broad mechanisms in logic were demonstrated for rule- governed procedures in laboratory practice, inductive logic for the classification of instruments, deductive logic for their ideal error free use, and abductive logic for troubleshooting errors (Sections 7.2 & 7.3). In the first case of logic, a classification of chemical analysis systems was induced from the unstructured lists of chemical analysis systems as given in textbooks (Section 7.2). From this classification, one system, namely spectrophotometry, was selected and subjected to analysis in the plane of expression (Section 7.2.2). The points of invariance identified in the expression line, were used as the points of departure to demonstrate mutual correlation between expression elements and content elements in the plane of content, and the semantic regress that knowledge work entails in the selection of appropriate instruments (Figure 7.6). In the second case of logic, these same points of invariance were used as the points of departure to demonstrate that deductive logic is used in the ideal use of the spectrophotometer, because there is an expected performance based on the rule, Beer’s Law, which is represented as data in instrument windows, printouts, and graphic inscriptions (Sections 7.3.1 & 7.3.2). Logic was explained as sign action, the 287 integration of theory, representation, and object, as given in the triadic sign model (Section 4.3.2) in practice situations. The third case of abductive logic was applied to the detection, diagnosis of errors in troubleshooting activities (Section 7.3.3). This was demonstrated as the recognition of a pre-interpretive factor in the environment, the symptoms and clues of experimental errors presented in instrument data windows, graphs, charts and statistics. It was demonstrated that multi-literacies are used in rule-governed laboratory procedures, and that the symbolic analyst can extract different kinds of information from graphs, charts and statistical representations of data, for the purposes of data evaluation, and quality monitoring (Sections 7.3.3.2 & 7.3.3.3). It was concluded that the ability to add value in method evaluation, innovation and improvement rests upon these skills in symbolic analysis. The analysis in Chapter 8 addressed pragmatics, rhetoric, and ideology in laboratory practice in response to the fourth research question: What range of competencies do knowledge workers need for contemporary clinical chemistry laboratory practices, and what additional skills will add value in EBLM? This analysis expanded on the knowledge fragment for spectrophotometry use, based on theoretical and technological considerations (Figure 7.6). It brought to the surface the pragmatic managerial factors that influence instrument selections, such as staff, space and budgets; and the value judgements that contribute to cut-off selections for laboratory tests used for disease diagnosis, prognosis and monitoring (Figure 8.2). It was demonstrated that symbolic analysis also applies to the framing of laboratory test information for making decisions about cut-off values, by making a trade-off between identifying too many false positives (specificity of tests) and too many false negatives (sensitivity of tests) (Figures 8.3-8.5). Rhetoric applies in communicating laboratory test information because graphical representations of data and statistics are subject to manipulations based on ideological biases and vested interests. It was argued from the semiotic perspective, that knowledge workers can discriminate among values, and can detect inappropriate biases and vested interests, captured in the way data and results are framed in graphs, charts and statistics. This skill in addition to discipline specific skills will add value in EBLM. Competence in clinical chemistry was thus represented along a continuum to cover this range of activities, from “d” discourse competence for operational effectiveness in the discipline, to “D” Discourse competence for critical assessments of laboratory tests in EBLM (Figure 8.1). This continuum approximates the range of 288 competencies demonstrated by skilled technicians and base level medical scientists, senior scientists and managers, and chemical pathologists respectively. The next section describes the contribution this thesis makes to knowledge and the advancement of higher learning in the medical sciences.

9.3 Contribution to knowledge and higher education

This thesis has proposed that the application of semiotics is useful for understanding and resolving some problems in contemporary medical science education. This proposition was tested in this research by addressing several research aims and questions made operational through a number of objectives (Section 1.4). The aims of this research have been met. Knowledge work and symbolic analysis have been described from a semiotic perspective, for contemporary clinical chemistry laboratory practices, including EBLM (Aim 1). A semiotic framework applicable to contemporary medical science knowledge has been distilled from a wide range of semiotic theories (Aim 2). Its effectiveness has been demonstrated by its application to the structure of clinical chemistry Discourse, knowledge and context, and to the logic, pragmatics, rhetoric and ideology that underpins laboratory practice (Aim 3). Details of the findings are presented in Section 9.2. In computerised laboratory environments, knowledge work entails symbolic analysis, in the interpretation of graphical and statistical representations of data, in order to extract different levels of information about laboratory test results and patients. It has also been demonstrated that symbolic analysis applies to social criticism in EBLM. The implications of these findings are discussed further in Sections 9.5 and 9.6. This thesis makes an original contribution to knowledge by demonstrating the application of semiotic theory to the structure of clinical chemistry knowledge, and to logic, pragmatics, rhetoric and ideology in laboratory practice. Because of its strong connections with science philosophy and scientific method, and its primary role as a tool for social criticism, semiotics has particular relevance for knowledge work in computerised laboratories, and socially accountable EBLM. It is unlikely that semiotics has been applied before in the medical laboratory sciences. Its importance has, however, been noted in other aspects of science education (e.g. Halliday, & Martin, 1993; Lemke, 2000). It is fitting at the turn of the twenty-first century when universities are being challenged to address the production of more 289 collaborative, transdisciplinary forms of knowledge (Gibbons, 1999), that semiotics be introduced into the medical sciences, to help make the transition between the disparate forms of knowledge needed for tackling complex EBLM problems. The semiotic analyses applied to clinical chemistry in this thesis have helped clarify the distinction drawn by literacy theorists, between the cultural competencies needed to meet the requirements of disciplines, and critical competencies needed to reflect on Discourses, for their social values to the wider community (Gee, 1996; Lankshear, 2000). In the medical sciences, particularly clinical chemistry, a distinction has been drawn between the expertise needed to produce high quality data and results in laboratory practices, and the socially critical perspectives needed for reflection on the value of laboratory tests in EBLM. A contribution is also made to semiotic theory by demonstrating its power as a tool of Discourse analysis in a field, namely medical laboratory science, in which semiotics is rarely if ever encountered. By the application of semiotics to the concerns of medical science education, this thesis makes a significant contribution to higher education in each of Boyer’s complementary levels of academic work (Boyer, 1990), the scholarship of teaching, the scholarship of integration, the scholarship of discovery (research), and the scholarship of application to real world problems (community service). As Candy (2000) explains, the four levels of scholarship aim to prepare graduates as knowledge workers and leaders in their fields, who can apply knowledge effectively, and transform work practices by critical evaluations, innovations and improvements. The scholarship of teaching is demonstrated in the way semiotics can be used to inform teaching practices, by making the knowledge work and multi-literacies used by experts in automated computerised laboratories more visible. The scholarship of teaching is also demonstrated by the expansion of scientific clinical chemistry knowledge to incorporate the pragmatic and social concerns of transdisciplinary knowledge systems such as EBLM. At the same time, the needs of lifelong learners are given equal consideration with the needs of employers. The scholarship of discovery and the scholarship of application are demonstrated in the synthesis of semiotic ideas tailored specifically to the concerns of clinical chemistry practice and teaching; and by demonstrating its wide application in many fields and sciences. Most significantly however, this thesis provides an example of the scholarship of integration, which as Candy (2000) explains, has at least three broad components, the integration of different knowledge claims within a field of study, referred to as 290 multidisciplinary; the integration of professional knowledge and use in real world settings, referred to as working knowledge; and the integration of insights drawn from other disciplines towards solving complex problems, referred to as crossdisciplinary and transdisciplinary. The semiotic analyses applied to clinical chemistry data in this thesis demonstrate that many disciplines are integrated in clinical chemistry; that working clinical chemistry knowledge entails the integration of theory and practice using sign logic, the integration of representations, concepts, ideas and theories, with the object world of experience; and that insights drawn from disciplines such as medical sociology can add valuable insights to laboratory test evaluations in EBLM.

9.4 Limitations, reliability and validity issues

There are constraints on the research process at the levels of theory, applications, and data sources. Chapter 4, which explains the semiotic framework used in this thesis, demonstrates the wide applicability of semiotics and its scholarly basis. It is not possible to do justice to the theorists, Saussure, Hjelmslev, Barthes, Peirce, Morris, and Eco (and also Foucault whose theory has been used to a lesser extent), within the limits of this thesis. It is hoped that any injustice has been compensated for by the demonstration of the power of semiotics to explain contemporary knowledge work in the medical sciences. The generalisability of findings is a requirement in many forms of qualitative research (Creswell, 1998; Krathwohl, 1998; Marshall, & Rossman, 1999; Yin, 1994). In other views, it is the unique social perspective provided in qualitative research that is valuable, and such perspectives might not be generalisable (Silverman, 2000). In this thesis, the latter perspective applies to the analysis of laboratory spatial arrangements, and the connotations of laboratory objects, because the analyses are based loosely around organising structuralist principles, and are dependent on the researcher’s unique perspective. The analyses are generalisable applied to the structure of laboratory instruments because their designs and use are governed by physical laws and principles. Whereas semiotics provides a general method for cultural analysis, applicable across a number of widely differing fields, languages, literature, myths, objects, spaces and buildings, its strong association with science philosophy, structuralism and scientific method means that it is widely applicable to all scientific 291 disciplines. Semiotic analyses applied to the wider sphere of culture, ways of life, art, language and literature, are usually unique contributions by cultural analysts operating rhetorically on data sources drawn from the sphere of culture in order to gain insights into human motivations and the workings of culture (Barthes, 1964/1973; Eco, 1976). When semiotic analysis is applied to scientific discourses such as clinical chemistry, with relatively well-defined knowledge bases, it can be expected that the analyses will be replicable at a number of levels because scientific logic applies. By necessity for rule-governed procedures, other clinical chemists will apply the same inductive logic for defining the catalogues of chemical analysis systems, the same deductive logic in the use of instruments, and the same abductive logic in error detection, diagnosis and correction, as illustrated in this thesis. The only thing in dispute is the nature of the logic, which is a matter for science philosophy to resolve (see Nickles, 1980). When semiotic analysis is based on scientific logic and pragmatics, rhetorical analysis and ideology criticism will also have a better chance of being replicated, although the unique perspective of the analyst will ultimately rule over semantic choices, as was demonstrated in the knowledge work of instrument selection (Figures 7.6 & 8.2). The semiotic analyses of clinical chemistry Discourse, and logic and rhetoric in laboratory practice, each constitute subject matter for an entire thesis, and are therefore necessarily given limited treatment as demonstrations of what semiotics can do. The demonstrations provided in Chapters 6, 7 and 8, were constrained to the availability of data sources, of which there were several collected from the teaching laboratory situation (see Sections 5.3.1 & 5.3.2). For the purposes of this thesis non- probability samples were selected theoretically and purposively to link the research questions deductively with aspects of the semiotic framework (Krathwohl, 1998; Yin, 1994). The data collection process is valid in the sense that ethical clearance was sought from the institution in which the research was conducted, and only anonymous student practical reports were used, or observations were made of laboratory practices limited to what was directly seen in instrument data windows or printouts. The main limitation of the data collection process is that the data source of greatest relevance to the findings of this research, student practical reports, have not been analysed for the evidence they contain of students’ multi-literacies or symbolic analysis capabilities. Only a few error samples have been drawn from this rich data source, but the project has been made “operational” in the sense that these data are 292 stored and available for further investigations in multi-literacies, or studies seeking answers to different questions (Krathwohl, 1998; Lemke, 1998b; Yin, 1994).

9.5 Further research

There are many avenues to explore when considering what takes place in classrooms. Socio-linguistic research in education for example, aims to account for differences in classroom experience based on gender, ethnicity, language and other factors. Such studies examine the way the learning experience is structured by pedagogic codes and institutional practices that favour certain groups at the expense of others (Bernstein, 2000). Some analysts of work suggest that the knowledge space governed by computers is not gendered, although access is limited for certain socio- economic groups (Aronowitz, & Di Fazio, 1994, p. 96). The major point for consideration in scientific disciplines is that people of all genders and races be given access to the “linguistic technology” of science, the specialised ways of speaking, performing, representing and communicating scientific knowledge (Halliday, & Martin, 1993; Lemke, 1990, 1998a, 2000). A major goal of science education is the enculturation of students into the ways of science, to assist them to adopt a “scientifically compatible worldview” which will allow them to participate effectively in the conversations of science (Cobern, 1991). This thesis has demonstrated from a theoretical perspective that value-adding in clinical chemistry laboratory practice is particularly reliant on multi-literacies, symbolic analysis of the multiple forms of representations, tables of data, graphs, charts and statistics, used to make assessments about laboratory results, tests and patients. Further research is needed, however, to assess students’ actual abilities in symbolic analysis and multi-literacies. Such research might be tackled from two broad perspectives, practical and theoretical. Firstly, from the practical perspective, Participatory Action Research (PAR) might be conducted in the teaching laboratory by including academics and students as participants, and by its extension into the workplace (Kemmis, & McTaggart, 2000). Secondly, theoretical research into the way meaning is acquired through the integration of representations, ideas and the world of experience might be conducted by drawing insights from semiotics (Eco, 1976; Lemke, 2000); and cultural anthropology integrated with psychology in so- called neo-Vygotskian research (Jacob, 1992; Laurillard, 2002; Lave, 1988). 293

From the practical teaching perspective, the research conducted in this thesis can be considered a primer for a major PAR project, seeking to investigate ways to improve students’ multi-literacy capabilities. Such research would involve a spiral of self-reflexive activities, involving teachers, students, and workplaces (Kemmis, & McTaggart, 2000). Given the outcomes of this thesis, the main focus of attention would be placed on laboratory inscriptions (following Latour, 1990); and the multi- literacies needed for cultivating students as symbolic analysts for contemporary clinical chemistry laboratory practice (following Lemke, 2000). There would be many ways to tackle the problem, using quantitative and qualitative data analyses. Interview data and questionnaires would provide students’ perspectives on their experiences and difficulties with handling laboratory inscriptions, and these would be complemented with the evidence of multi-literacies documented in their practical reports. Phenomenographic analysis (Marton, Hounsell, & Entwistle, 1997) of these data would derive inductively, descriptions of what students actually do with inscriptions, as complementary to the semiotic perspective of what can be expected of knowledge workers and symbolic analysts, and the multi-literacies used. Content analysis (Manning, & Cullum-Swan, 1994) of existing data in the form of students’ practical reports, could be used to quantify the extent of past students’ problems in handling laboratory inscriptions, and provide a basis for pre and post-test analysis following any proposed intervention (Krathwohl, 1998). Whereas PAR will be strengthened if the workplace is included (Kemmis, & McTaggart, 2000), or a multi-site case study is applied (Yin, 1994), there is an alternative although related avenue for investigation, the matter of workplace or Work-Based Learning (WBL) (Boud, 1998; Boud, & Solomon, 2001; Symes, & McInytre, 2000). Proponents of WBL propose that the workplace provides the subject matter needed for a professional curriculum because valuable working knowledge is acquired on the job through training and experience. Taking a more pragmatic perspective, as accessibility to automated instruments becomes increasingly difficult due to the limited availability of parts and services, space, and budgets, WBL presents an increasingly attractive option for undergraduate medical science education, at least in clinical chemistry. It will be necessary, however, to maintain academic input in workplace courses to ensure that workplace knowledge is academic as well as experiential. This means that universities are more than ever 294 important, because workplaces are more than likely pre-occupied with productivity, efficiency and service issues (Boud, & Solomon, 2001). From the theoretical perspective, there are avenues for exploration in semiotics, and cultural anthropology and psychology. In applying semiotics, this thesis has worked with sign systems, the interrelations between signs, their codified or cultural meanings and their interpretations, but there has been little discussion of the way signs signify in their specificities as icons (resemble objects pictorially), indexes (make direct reference to objects by pointing at them) or symbols (draw metaphorical, arbitrary analogies with objects). Research would be useful that on the one hand, compared the degrees of iconicity and symbolism used in the different medical sciences, for example haematology, cytology and histology which study the morphologies of tissues and cells; as opposed to clinical chemistry which makes extensive use of abstract mathematical and chemical symbols, schematic diagrams, charts and graphs. Guidance for such a study would be sought from a number of theorists who address iconicity and symbolism in the sciences (Eco, 1976; Kress, & van Leeuwen, 1996; Lemke, 1999, 2000; Peirce, 1931-1958; Sebeok, 1994). Lemke’s (1999, 2000) research into typological and topological meaning provides a point of departure for such research. In clinical chemistry, symbolic analysis of abstract symbols in graphs, charts and statistics provides the main focus of attention. The terms typological and topological are applied respectively, to the discrete (numbers) and continuous variation (graphs) by which meaning is extracted from inscriptions in the sciences (see Sections 7.3.3 & 8.4). Vygotsky’s research (1994) provides another point of departure, in the suggestion that practical intellect and symbolic activity are not unified until secondary education. Research could thus be directed towards how well this unification has been achieved by students entering the tertiary sciences. Such research might be able to explain the general perception in the medical sciences, that the iconic disciplines such as haematology are more popular than the abstract symbolic disciplines such as clinical chemistry. It would be simplistic to explain this problem as a shift in the media of communication away from print media towards the iconic forms of film and television because, as some researchers argue, in the information age, print literacy is needed now more than ever (Beckett, 2000). This line of research would also connect with research into applications of critical literacy theory (Lankshear, 2000). 295

9.6 Recommendations

The recommendations arising from this investigation into knowledge work in contemporary clinical chemistry practices in the medical sciences are directed towards universities, the pathology industry, the professions, and theoreticians. Higher education and science education researchers (Bruce, & Candy, 2000; Candy, 2000; Gerber, & Lankshear, 2000; Laurillard, 2002; Lemke, 2000) recommend that more deliberate attention be given to cultivating knowledge workers who can navigate the increased information load made accessible by computers in a discriminating and critical fashion. In the sciences in particular, attention must be given to the multi-literacies used in symbolic analysis of the abstract symbolic forms in which data and information are given. In clinical chemistry, these forms are commonly tables of data, graphs, chart and statistics. Higher education researchers argue that students need assistance in acquiring these multi-literacies, they are not acquired automatically (Beckett, 2000; Kress, & van Leeuwen, 1996; Laurillard, 2002; Lemke, 2000). Collaboration is needed between the university sector, pathology industry and relevant professions (e.g. Australasian Association of Clinical Biochemists, and Australian Institute of Medical Scientists) in order to consider the possibilities of workplace learning degrees (Boud, 1998; Boud, & Solomon, 2001). An analysis of work is needed to assess the multi-literacies actually used in automated computerised laboratories, and the knowledge needed in information theory, given the increasing role played by diagnostic Expert Systems. Information literacy (effective knowledge navigation) and multi-literacies (symbolic analysis) are important because the evaluations of laboratory tests in EBLM, will require even more advanced skills in symbolic analysis than those used to assess laboratory data and results. The evaluation of laboratory tests requires, however, much more than statistical analysis of existing information, socially critical perspectives are also needed, for which medical sociology provides valuable insights (e.g. Petersen, & Bunton, 1997). There are possibilities for the cross fertilisation of ideas proliferating from a number of sources: between semiotics and the cognitive sciences on the matter of expertise and competence in disciplines, for work with coded science contents, and designs of Experts Systems (Chi, Glaser, & Farr, 1988; Gillies, 1996); between the sciences and the humanities bridged by semiotics, in the production, transmission, acquisition and learning of transdisciplinary clinical chemistry 296 knowledge; between the health sciences and the life sciences as exemplified in new research institutes such as the Institute of Health and Biomedical Innovation (IHBI, n. d.). There is as yet a wealth of understanding and knowledge to be acquired from the treasure trove of semiotics. If the method at first appears to be complex and difficult it is because science education at the tertiary level is not doing the job of inducting people into the culture, history, philosophy and methods of science (Cobern, 1991; Halliday, & Martin, 1993; Lemke, 1990; Matthews, 1998; Schwab, 1962), but operating on the surface using scientific techniques applied to clearly defined problem areas requiring technically rational modes of reasoning (Schön, 1983). Semiotics can be used to redress this imbalance by giving students insights into the structure of scientific disciplines; the logic and pragmatics applied in scientific practice; and by introducing them to social criticism as conducted in humanities disciplines. More specifically a course in biosemiotics (including zoosemiotics and phytosemiotics) which studies the signs of life, integrating biology and semiotics, life processes, and the patterns of mind (Sebeok, 1994), would expand the horizons of students engaged in all biomedical science courses. It is not just that biosemiotics provides a way to model life processes, integrating genetics, morphologies, behaviours, and environmental features; it also incorporates a critical function by exposing inappropriate ideological bias in biological knowledge, in ways analogous to Derrida’s (1967/1974) deconstruction of logocentric bias, ethnocentrism, as delivered through the word in language and literature.

9.7 Conclusion

A semiotic framework has been applied in this thesis to contemporary clinical chemistry knowledge, knowledge work and symbolic analysis in automated computerised laboratories and EBLM. From the semiotic perspective knowledge workers understand the structure of the discipline and its interrelations with other disciplines; apply logic in laboratory practices, in classificatory procedures, instrument use and in troubleshooting errors; and can discriminate among values and recognise that rhetorical devices are used to frame laboratory test information for its communication to the users of pathology services. It is proposed that the terms knowledge work and symbolic analysis apply to highly skilled work in computerised 297 laboratories, and that multi-literacies are needed for manipulating and interpreting abstract symbolic forms of information about laboratory results, tests and patients. These skills are needed for complex method evaluations, innovations and improvements, and can also be expanded to laboratory test evaluations in EBLM, although insights from socially critical disciplines such as medical sociology will also be useful. This thesis provides a theoretical perspective of work from the academic sector, and an idealised picture of what knowledge workers and symbolic analysts do in automated computerised laboratories. An analysis of work conducted by spending an extended period of time in the industry is needed to provide a real world picture of what knowledge workers and symbolic analysts actually do. But such an analysis would also have to address certain questions: Is knowledge subordinated to the productivity and efficiency goals of the industry? Are medical scientists being de-skilled and/or displaced by automation, robotics and informatics? If so: What is their plight as lifelong learners? And, what will be their contribution to EBLM? 298

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Appendix A. Australasian Association of Clinical Biochemists Board of Examiners Syllabus (AACB syllabus)

Analytical Biochemistry

Candidates should be familiar with the theoretical Volumetric Methods, including complexometric principles and techniques underlying the full titration. range of clinical biochemical analyses. Emphasis should be placed upon the factors Gasometric Methods which govern the choice of method and on the evaluation of instruments and methods. Electrometric Methods

pH, including CO2 responsive systems. The headings and subsections mentioned below Other ion-sensitive electrodes, for example, Na+ indicate those methods, instruments, and K+, Ca++ analytical techniques, whose principles should Redox, including O2 responsive systems. be understood. Knowledge concerning the Titrations, potentiometric, amperometric, and physico-chemical principles and rationale behind Conductimetric. the basic design (rather than precise technical The use of such methods in dynamic situations details) of the instruments, and methods of such as enzyme rate determination and for assessing their performance, may be expected. semi-automatic analysis should be understood. Candidates should be able to discuss the uses Ion selective electrodes of the different classes of instruments and the Biosensors relative merits which lead to their selection in various analytical situations. Chromatographic and Electrophoretic

The candidate must obtain adequate laboratory Techniques, both quantitative and qualitative experience which is the basis of successful applications. study. Practical experience is the main guide to Various supporting media, for example, paper, understanding which topics should be known in membranes, gels, thin layer, ion exchange detail or in outline only. resins, molecular sieves, disc, column, continuous flow, and others. General Physical Techniques Immunoelectrophoresis, conventional, medium and high voltage electrophoresis. Fractional distillation, for example, preparation of Isoelectric focusing. Solvents. Gas and high-pressure liquid chromatography. Reverse osmosis. Sample preparation, dialysis, desalting, Solvent extraction, partition coefficients. concentration, preparation of derivates. Ultra-centrifugation. Capillary electrophorsis. Freeze-drying.

Mass spectrometry (including tandem). Preparation of high quality water Immunochemical Techniques Qualitative techniques for identification of Photometric Methods proteins. Quantitative techniques for measuring Absorptometry. concentration of specific proteins, radial Spectrophotometry. immunodiffusion, nephelometry or turbidimetry. Fluorimetry. Laurell rocket, cross-immunoelectrophoresis. Flame spectrometry. Competitive binding techniques, Nephelometry. radioimmunoassay and immunoassay using Turbidimetry. non-isotopic labels, for example, enzymes. Atomic absorption spectrophotometry, flame and Enzyme immunoassay (see 3.10). Flameless. Standardisation of these techniques and their Flame emmission spectroscopy use for various proteins in body fluids. Inductively coupled plasma emission. Homogeneous assays ICPMS.

324

AACB syllabus

Isotope Techniques Analysis of Laboratory Error/Statistics Basic physics of stable and radio-isotopes. Concepts of reference range, analytical error, Counting techniques and their statistical biological variation, and various simple appraisal. Units of radioactivity. Concepts of parameters for describing data (mean, mode, physical and biological half-life. Laboratory and standard deviation, confidence limits, patient hazards. Safety standards. Principles of variance, tests of significance). radioactive detection and counting. Applications to: assessment of inaccuracy and imprecision, errors of instruments, Legal requirements for storage and disposal and pipettes, and other equipment. permission to administer to humans. Use in Quality control methods. quantitative and qualitative analysis, for example, single and double isotope dilution techniques, Trace Elements. autoradiography. Radio-ligand assay. Use in Zinc, Copper, Aluminium, Lead, Arsenic, radioimmunoassay. Problems of purity of Chromium, Cadmium, Mercury. labelled compounds, storage and specific activity. Immunoradiometric assay. Labels. Quantities and Units Functional sensitivity of an assay. SI Units - their advantages and disadvantages. Interference in assays – e.g. heterophile antibodies. Specimen Collection, Preservation, and Preparation for Analysis Osmometry Constituent stability. Methods of measurement, for example, Documentation and specimen flow systems. Depression of freezing point. Interferences in the collection process. Osmolar gaps

Enzymology Enzyme kinetics (Km) Fixed incubation and kinetic methods. Enzyme units. Enzyme standards; Standardisation of methods. Enzyme immunochemistry. Enzyme multiplied immunoassay. Enzyme linked immunosorbent assay.

Mechanised Techniques and Work Simplification Robotics Principles of continuous flow systems, single and multichannel. Discrete analysis systems, fast centrifugal analysers. Use of recording and digital output instruments.

Laboratory Data Processing and Computing Use of computers for data collection, processing, And as management tools. Expert Systems

325

AACB syllabus

Clinical Biochemistry

The clinical biochemist should have an Diabetes Mellitus and Hypoglycaemia understanding of the major biochemical Substrate, neural, and endocrine regulation of abnormalities found in disease and their methods insulin and glucagon secretion - the gut as an of detection in the laboratory. They should have endocrine organ. a general knowledge of the interpretative aspects Diagnosis of diabetes mellitus and monitoring by of clinical biochemistry. glycated haemoglobin and urine albumin excretion. Somatostatin. C-peptide. A Clinical Biochemist should understand the Glucose tolerance, glycosuria. Plasma insulin principles of testing the more important and glucagon measurement. biochemical and physiological functions of Differential diagnosis of coma: ketoacidosis, organs or organ systems and be able to advise lactate acidosis, hyperosmolar coma, and hyper- clinicians on their performance. They should take glycaemia. every opportunity to consult with clinicians to Diagnosis of insulinoma and other causes of improve their understanding of the clinical hypoglycaemia; use and dangers of provocative manifestations of disease. tests, for example, tolbutamide and glucagon. Classification and diagnosis of DM. Water and Electrolytes Value and limitations of plasma insulin assays in Distribution of water and electrolytes. hypoglycaemia and hyperglycaemia. Measurement of plasma volume, total body Glycosylated haemoglobins and proteins. water, sodium and potassium spaces. Causes of hyper and hyponatraemia and hyper Diagnostic Enzymology and hypokalaemia. Types of assays, enzyme determination in Osmolality. Hyperosmolar coma serum, urine, and cells. Plasma and urine osmolality. Stability of enzymes. Shock. Metabolic effects of trauma Diagnostic use of enzymes. Diagnosis and quantitative assessment of water Use of isoenzymes. and electrolyte loss. Diuresis; pharmacological – osmotic Proteins: The Serum Proteins in Health and measurement of intracellular electrolytes. Disease Distinction between diabetes insipidus and Interpretation of electrophoretic protein patterns, compulsive water drinking. recognition of paraproteins and their further Syndrome of inappropriate ADH. investigation by immunological techniques. Causes of hypoalbuminaemia. Assessment of Respiratory Function: H+ Metabolism protein-losing enteropathy and renal loss of + H (pH), pCO2, pO2, oxygen saturation. protein. Effects that malnutrition and Lactate and pyruvate levels. malabsorption can have on protein state. Simple lung function tests. Disorders of immunoglobulins and changes in Assessment of body deficit or excess of H+ specific proteins, for example, alpha1-antitrypsin, Understanding of acid/base disorders. transferrin, and their use in diagnostics. Urine proteins, including Bence Jones protein. Renal Function Protein selectivity as an indication of renal Clearance tests: measurement of glomerular damage. filtration rate and renal plasma flow. Tubular function tests: concentration tests, Gastric Function ammonium chloride loading tests, amino acid Stimulation tests using pentagastrin and insulin. chromatography, and renal glycosuria. Secretory levels in pernicious anaemia, peptic Normal and abnormal urine composition ulcer, neoplastic disease, and Zollinger- including abnormal pigments. Ellison syndrome. Proteinuria, differential protein clearance. Urea breath tests Renal failure. The nephrotic syndrome. Renal Calculi.

326

AACB syllabus

Intestinal Function Parathormone. Absorption tests, for example, glucose, xylose, Calcitonin. fat, lactose, iron. Significance of the concentration of calcium, Pancreatic enzymes, secretin and pancreozymin phosphate, and magnesium in plasma. Urinary test, Lundh test. excretion of calcium and renal tubular handling of Disaccharidases. calcium and phosphate. Bile salts. Elastase. Alpha 1 antitrypsin and Differential diagnosis of hypercalcaemia and albumin loss. Occult blood. hypocalcaemia. Balance studies (associated with food and urine Pathogenesis of renal stones. analyses). Metabolic bone disease. Faecal analyses; fat, nitrogen, sugars (in Magnesium metabolism; causes and effects of children). deficiency. Electrolytes, including analyses of ileostomy Collagen crosslinks eg deoxypyridinoline. fluid. Sweat tests. Endocrine Function Hydrogen breath testing. The hypothalmic-pituitary-adrenal axis. Stimulation tests employing insulin, glucagon, Liver Function metyrapone, and ACTH. Dexamethasone Metabolic disturbances in liver disease. suppression. Differential diagnosis of Cushings Bilirubin and conjugated bilirubin. Syndrome and Addisons Disease. Feed back Urobilinogen. mechanisms and control of menstrual cycle. Urobilin. Investigation of amenorrhoea. Clomiphene and Enzymes in liver disease, for example, alkaline GnRH tests. Biochemical changes during and phosphatase, γ -glutamyl transferase. monitoring of pregnancy. Aminotransferases Investigation of infertility in males and females. Protein synthesis, particularly albumin. Investigation of hirsiutism in females. Immunoglobulin changes in liver disease. Monitoring of IVF therapy. Alphafetoprotein Diagnosis of thyrotoxicosis and myxoedema. In Cholesterol. Bile salt metabolism. vitro and in vivo function tests. Use of TRH. Bromsulphalein excretion test. Growth hormone and prolactin. Differential diagnosis of disease producing Stimulation and suppression tests. jaundice. Somatostatin. Hypothalmic releasing and Diagnosis of non-icteric liver disease. inhibitory hormones. Serological and PCR markers in diagnosis and Steroid synthesis and metabolism. Congenital monitoring of liver disease (Hep A,B,C). adrenal hyperplasia. Mechanisms of hormone action, receptors, cyclic Lipids AMP, and cyclic GMP. Biochemical basis and limitations of Transport of hormones. classifications of lipoprotein disorders. Renin-angiotensin-aldosterone system. Genetic and acquired disorders of triglyceride, Hormones of the gastro-intestinal tract. lipoprotein, and cholesterol metabolism. Hyper and hypolipoproteinaemias. Nutrition Theories of atherogenesis and coronary heart The digestion of proteins, carbohydrates, and disease. lipids and the biological role of vitamins and the Investigations and principles of treatment of trace elements. hyperlipidaemias. The nutritional concept of protein quality and its Lipoprotein (a). assessment by measurement of biological value and nitrogen balance; also the importance of these Calcium, Magnesium and Bone factors in patients on synthetic diets. The laboratory methods of assessing vitamin status Properties and actions of parathyroid hormone, and measuring trace elements. calcitonin, and Vitamin D. Metabolism of vitamin D to its hormonal form. Regulation of secretion of hormonal vitamin D.

327

AACB syllabus

Inborn Errors of Metabolism Toxicology and Drugs Possible defects in protein biosynthesis arising Detection and quantification of common drugs in from genetic mutations. Quantitative and therapy, for example, digoxin, lithium, and qualitative enzyme abnormalities occurring in anticonvulsants. Overdosage, for example, genetic disorders. The biochemical salicylates, paracetamol, and barbiturates, and consequences of a primary enzyme block in a suspected addiction, for example alcohol, metabolic pathway and the ways in which clinical morphine, morphine derivatives, and and pathological signs may be produced. amphetamines. Methods of detecting metabolic disorders, with Differential diagnosis of coma. Metabolic effects particular consideration of screening selected of ethanol. clinical groups, for example, the mentally Environmental hazards, for example, lead, subnormal and the newborn. Evaluation of mercury. detection programmes. Antenatal diagnosis. Methods of treatment, particularly by dietary Cerebrospinal Fluid restrictions and vitamin supplementation, and the Glucose, differential proteins, enzymes. biochemical monitoring of the treatment. Oligoclonal bands. Consideration of the following conditions: Amino Tau-transferrin. acid disorders, especially those involving phenyl alanine, tyrosine, methionine and homocystine Amniotic Fluid metabolism, and the transport disorders, Bilirubin, creatinine content. cystinuria and Hartnup disease. Presence and significance of alpha-fetoprotein. The organic acidaemias, particularly Lecithin/spingomyelin ratio, palmitate, and other methylmalonic and propionic acidaemia. tests of foetal lung maturity. Glycogen storage disease, galactosaemia, and Screening for Down syndrome. hereditary fructose intolerance. The porphyrias. Enzyme deficiencies resulting in The Biochemical Effects of Neoplasia haemolysis, especially glucose-6-phosphate Effects of tumors, both anatomical and dehydrogenase and pyruvate kinase. pathological. Adrenogenital syndrome, fibrocystic disease. Tumor markers, their biochemical and Wilsons disease. pathological significance and their use in Mucopolysaccharide disorders, cerebral management of benign and malignant tumors. lipidoses, and metachromatic leukodystrophy. Some examples of this are: alpha fetoprotein, hCG, Disorders of purine metabolism, hyperuricaemia, CEA, ectopic production of hormones and the gout and the Lesch-Nyhan syndrome. syndromes these cause. Cystic fibrosis. Molecular Biology Haem and Porphyrins Principles of PCR, Northern, Southern and Structure and chemistry of porphyrins. Western Blots. Biosynthesis – delta-aminolaevulinic acid, ALA Testing for common diseases (HFE, CF) synthetase. Classification of porphyrias - hepatic, Cardiac Markers erythropoietic. Other causes of porphyria (lead Troponins, CK-MB, CK-isoforms, myoglobin. poisoning, anaemia). Homocysteine Catabolism of haem, formation of bile pigments. Measurement of porphobilinogen, uro and coproporphyrins. Spectroscopy for haem pigments. Measurement of haemoglobin and detection of abnormal forms. Investigation of porphyria including appropriate specimen collection and preservation. Haemochromatosis.

328

AACB syllabus

Laboratory Management

It is not expected that the candidate will have Quality Management Systems. had the opportunity to become fully conversant ISOguide 25, ISO 9000 systems. with all details of laboratory management. The Efficiency of Laboratory Testing Strategies. Clinical Biochemist should however, have a Diagnostic sensitivity, specificity and efficiency of reasonable knowledge of the important aspects tests, ROC curves. of the following: Evidence Based Medicine

Organisation of a clinical biochemistry laboratory, SPECIAL PROJECTS including routine and emergency services. Short-term projects involving a considerable Screening and profiling. number of skills required of a laboratory supervisor (analytical, instrumental, evaluative, managerial, Staff training, performance management, and organisational) should be undertaken to encourage work assignment. initiative and independence. The candidate should acquire an ability for clear report writing and should Laboratory safety including chemical, radiation, be encouraged to write assays on topics for physical and biological hazards. discussion with his supervisor.

Reagents and apparatus, their selection, sources EDUCATIONAL ACTIVITIES of supply, and techniques for assessing the The candidate should plan his course of study in quality of equipment and reagents. preparation for the Membership examination in consultation with their supervisor and the State Budget preparation and monitoring. Branch Education Representative.

Presentation of results of biochemical analysis, The candidate should attend the following activities reports of results. as an adjunct to higher own studies and practical experience. Laboratory design. (a) Regular seminars on clinical Quality control implementation, monitoring, biochemistry run by the Association. performance evaluation. (b) Appropriate lectures, seminars, Laboratory statistics. Use of mean, mode, discussions or case presentations held in median, standard deviation, variance, standard hospitals or other institutions. error of mean, analysis of variance, F-test, t-test, and non-parametric statistics. (c) The annual Course in Chemical Regression analysis. Pathology held jointly by the Association and the Royal College of Pathologists of Australasia. Method Comparison. Use of regression analysis, Bland-Altman plots. (d) The Annual Scientific Meeting of the NCCLS and AACB guidelines for method Association. comparison. (e) Use of the Internet e.g. web sites of the Determination of Reference Intervals. AACB, AACC, ACB. Sample selection. Statistical analysis (detection of outliers, sample size consideration). (f) Use of Medline and other searches. Parametric and non-parametric methods including confirmation of Gaussian distribution.

329

Appendix B. Undergraduate Clinical Biochemistry Course Materials

B1. Clinical Biochemistry Unit Outlines

Introduction: The main objective of this course of study is to strengthen the scientific approach already inculcated into students and to extend it into the area of clinical biochemistry. This course of study will provide graduating scientists with sufficient biochemical knowledge and laboratory experience to allow them to work effectively in both the smaller general-purpose laboratory performing a limited number of biochemical tests and the larger specialised laboratory performing in-depth studies of all aspects of clinical biochemistry.

The first unit (first semester) will be approached in terms of clinical tests associated with various groups of analytes such as carbohydrates, lipids, and proteins or with various body organs such as liver, kidney and pancreatic function tests. The second unit (second semester) will be approached in terms of clinical tests associated with various groups of analytes such as enzymes, electrolytes, steroids or with various bodily organs such as adrenal and thyroid glands. Automation of laboratory tests is a significant part of this second unit.

These approaches will be used in preference to a more clinical/medical approach where a particular disease would be studied and all the various analytes considered which are of diagnostic relevance.

Knowledge of biological chemistry, physiology and laboratory technology derived from prerequisite subjects will be reinforced and used as a basis to appreciate the reason(s) that a particular analyte is measured and thus to interpret results, and to understand the chemical and/or physical basis of the various methods that are available for the estimation of that analyte. While methodology will be of upper importance, physiological and pathological aspects will also be emphasised. Knowledge of statistics, derived from a prerequisite subject will be used in the areas of methodology and the selection of normal ranges. 330

Clinical Biochemistry 1 Lectures Clinical Biochemistry 2 Lectures I Introduction: I Enzymes: 1. Collection of specimens 1. Introduction; general properties 2. Alk. and acid phosphatase (ALP,ACP) 2. Errors and variation 3. 5'-Nucleotidase, aminotransferase 3. The “normal range” – Reference Interval (5'NT,AST,ALT) 4. Protein interference in clinical methods 4. Lactate dehydrogenase (HBD) II Renal Function Tests 5. Creatine kinase (CK),γGT, cholinesterase 5. Function of kidney II Electrolytes: 6. Composition of urine 6. Introduction 7. Urea 8. Creatinine and uric acid 7. Estimation of Na+, K+, Cl- 9. Clearance tests; Proteinuria; Calculi 8. Iron; iron-binding capacity 10. Amino acids and derivatives; Genetic 9. Calcium, phosphorus metabolism Disorders – Inborn errors of metabolism 10. Calcium, phosphorus estimation III Carbohydrates 11. Magnesium, copper, lead, etc 12. Acid-base regulation 11. Glycogen metabolism 13. Assessment of acid-base balance 12. Estimation of glucose; 14. Assessment of acid-base balance 13. Glucose tolerance; ketone bodies; non- glucose sugars in urine; III Automation: Glycogen storage diseases 15. Principles of computerisation & mechanisation IV Gastrointestinal and Pancreatic 16. Discrete and continuous methods. (exocrine) Function Tests Centrifugal analysers; Random/Selective 14. Function of pancreas, enzyme tests of access analysers function – amylase 17. Recent advances in automation, dry 15. Lipase; functional tests - faecal trypsin, chemistries (Auto and non-auto) duodenal intubation, absorption tests IV Toxicology, drug monitoring (18) 16. Gastric function tests V Thyroid Function: V Porphyrins (lecture 17) 19. Metabolism of I-containing substances VI Liver Function Tests 20. Thyroid function test 18. Function of the liver; tests of excretory function; bilirubin VI Function of adrenal medulla: 21. Catecholamines 19. Excretory function – urobilinogen

Metabolic function – carbohydrates, lipids, VII Steroids: proteins, vitamins, metal ions 22. Structure and metabolism 20. Detoxification and haematological Steroids and the adrenal cortex functions; Enzymes in liver disease 23. Stimulation and suppression tests VII Proteins 24. Male and female sex hormones 21. Plasma proteins, estimation of proteins 22. Abnormal levels of proteins in the body VIII Quality Control (25) VIII Lipids 23. Lipoproteins 24. Lipoproteinemia, IX Vitamins, CSF (26) Cholesterol 25. Phospholipids; Fatty liver 26. Triacylglycerols, Fatty acids, Mucolipidoses; L/S ratio 331

Clinical Biochemistry 1 Practicals Clinical Biochemistry 2 Practicals

Weekly Experiments: Weekly experiments: 1. Accuracy and Precision Experiments 1. Alkaline Phosphatase (ALP), Aspartate 2. Protein Precipitation and Estimation Aminotransferase (AST) and Acid 3. Urea and Creatinine Estimations Phosphatase (ACP) 4. Uric Acid Estimations and Analysis of 2. Creatine Kinase (CK), Lactate Renal Calculus Dehydrogenase (LDH) and Isoenzymes of 5. Estimation of Unknowns I LDH 6. Estimation of Glucose; GTT; Urinary 3. Electrolytes and Iron Glucose and Glycohemoglobin Estimations 4. Estimation of Unknowns I 5. (a) Lactate Dehydrogenase (Continuous 7. Lipase and Amylase Estimations kinetic method) (b) Copper (c) Blood Gases 8. Estimation of Unknowns II (d) Instrument Video (Spectrophotometry) 9. Bilirubin & 5-ALA Estimations 6. (a) Random Access Analyser (Cobas Mira) 10. Protein Electrophoresis and Densitometry; (b) Centrifugal Analysers (Cobas Fara and Total Protein & Albumin Estimations Flexigem) (c) Instrument Video (Hitachi 911) 11.Total & HDL Cholesterol Estimations, 7. (a) Kodak DT60, DTSC and DTE (b) Lipoprotein Electrophoresis Reflotron Analyser (c) AccuSport (d) 12. Triacylglycerol and L/S Ratio Estimations Instrument Video (Dry Slide + Roche 13. Estimation of Unknowns III Modular) 8. Estimation of Unknowns II

9. (a) Abbott TDx (and IMx) Analysers (b)

Curve fitting (c) Instrument Video (Hitachi

747)

10. (a) Barbiturates (b) Toxilab

11. (a) Thyroid Function Tests (b) Instrument Video (Cobas Integra, ACA Star, Dimension ) 12. (a) HPLC – Catecholamines and VMA (b) Gas Liquid Chromatography 13. (a) Cortisol (b) Stratus (c) Quality Control

332

B2. Glucose Practical Protocol

333

B3. Glucose Master Report

334

335

336

337

338 B4. Experiment Stages

Stock Standard & dilutions → S5 – S1 T1 T2 T3 QC Patient samples Reagents

BL S1 S2 S3 S4 S5 T1 T2 T3 QC Reaction mixtures Cuvette/cell

Data window

Mode

Wavelength

Reference

Sample input

Absorbance data A = εcL

S1 = 0.05 = 2 g/L y = b0 + b1x S2 = 0.10 = 4 450 nm 450

S3 = 0.15 = 6 λ R2 = 1 S4 = 0.20 = 8

S5 = 0.30 = 10 A @ B0 = o B1 = 1 T1 = 0.07 [c] T2 = 0.15 T3 = 0.35 QC QC =0.25 CV = s/⌧ %error = o – e x 100% e 339

B5. Student Glucose Practical Report

340

341

342

343

344

B6. Barbiturate Practical Protocol

345

B7. Barbiturate Master Report

346

347

Appendix C. Instrument Printouts

C1. Serum Spectral Scans

348

C2. Barbiturate Scan – Expected Result

349

C3. Barbiturate Scan - Sample Unwashed

350

C4. Barbiturate Scan – Extraction Loss

351

Appendix D. Glucose Practical Report Data Summary

Serum glucose estimation by two methods

References: Glucose Practical Protocol, Appendix B2. Glucose Master Report, Appendix B4. Student Glucose Practical Report, Appendix B5. Data analysis, Section 5.3.3.2. Practical report requirements: Overview - glucose estimation by two methods; calculations and data validation; method comparison; and description of the chemical bases of the methods. Calibration - Standard curve preparation; assessment of data points by visual graph inspection and regression analysis, r2 and intercept; assessment of compliance of data with Beer’s Law; assessment of the validity of the assay by QC accuracy, % error. Interpretation of results - consideration of appropriate reference ranges, fasting status of patient, quality of specimen, source of specimen, venous or capillary blood. Discussion - limitations of methods, specificity, sensitivity, accuracy and precision; specimen quality, haemolysis, icterus or lipemia; comparison of methods with respect to performance, observed, and expected. Summary of demonstrator comments re data validation in practical reports: A wide range of errors in graph interpretations and regression analysis were noted by demonstrators: r2 was identified but not the intercept; in cases in which graphs did not go through zero (x-y intercept), there was little comment on the cause of the error or observation that Beer's Law was not obeyed; a graph was interpreted as “very linear” and then the statement was contradicted by r2 = 0.94 (see Appendix B5); a case of r2 = 0.9 was accepted without the acknowledgement that random error was indicated; there were cases of regression analysis, r2 = 1.532 (maximum r2 = 1), and r2 = 0.076, reported and yet direct inspection of the graphs in each case indicated very good correlation (r2 ≈ 1). Six error scenarios have been redrawn in the graphical set below plus statistical analyses which were incorrect or omitted, to illustrate errors in data validation. In the first scenario, a line of best fit is applied to very scattered data, for which random error is indicated, and Beer's Law is not obeyed. The 352 regression analysis, e.g. r2 = 0.976, in this case would have supported the assumption of random error (Graph (a), random error). In the second scenario, the data points are fitted in a zigzag fashion so that the expected linearity for MAS data is not considered or that data are invalid (Graph (b), misinterpreted data). In the third scenario, curvilinear data are indicated but a line of best fit has been drawn, or a line drawn arbitrarily through zero and one of the points (Graph (c), curvilinear data). Regression analysis (e.g. r2 = 0.976), helps support a claim that data do not obey Beer’s Law and are therefore invalid. In the fourth scenario, the graph is extrapolated beyond the fourth standard point above which linearity is lost, which means the assumption has been made that Beer’s Law is obeyed beyond that which is demonstrated (Graph (d), loss of linearity). Regression analysis would assist in the validation of data in such cases, for example, r2 = 0.992 versus r2 = 0.998 when the renegade point is omitted. In the fifth scenario, the line fits all the points except zero, but a line has been drawn arbitrarily through one point and zero (Graph (e), intercept error). Regression analysis would in this case support the interpretation that an intercept error is present, and that all data points are on the line but it does not pass through zero. In the sixth scenario, an outlier is probably present because all points are on a straight line through zero if the renegade point is omitted, as is reflected in the regression analysis (Graph (f), outlier).

y y y 2 2 r = 0.967 r = 0.976 * * * * * r2 NA * * A * A * * * * * * *

[c] x [c] x [c] x (b) Misinterpreted data (a) Random error (c) Curvilinear data 2 y y y r = 0.993 r2 = 0.998 r2 = 0.985 b0 = 0.003 * r2 = 0.992 * * * * A A r2 = 1,000 A * r2 = 0.999 * b = 0.063 * 0 * * * * * * *

[c] x [c] x [c] x (d) Loss of linearity (e) Intercept error (f) Outlier

Figure 1. Graphical interpretation of data. 353

Clinical interpretations: The term “normal range” was used by students instead of “an example reference range”, and no statement was made about the patient’s fasting status by most students. No comment was made about whether the reference range used was relevant for the method or if it referred to fasting levels. Neither was it stated whether it referred to whole blood or plasma, and some students did not use SI units, mmol/L, but mg/dL. Discussion: Many students did not consider the factors affecting accuracy and precision of methods, limitations of methods or comparison of methods. They did not account for discrepancies in results between the 2 methods. Those students simply repeated the discussion about clinical significance. Some students reported regression analysis as r2 = 9.99 x 10-1 (the form given by the computer program LS-Fitter), and by not converting it to 0.999, misunderstood its meaning. More students are getting the message about r2 and intercept for validating data and Beer’s laws compliance, but some are simply stating the values, and not making interpretive comments about Beer’s Law compliance with respect to the linearity of their data. Students do not acknowledge the assumption that Beer’s Law is obeyed when they accept scattered data. Professional and technical issues: The report presentation and graphs are much improved on previous effort overall.