Species of Science Studies

Paul Alexander Armstrong

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Department of Sociology University of Toronto

Species of Science Studies

Paul Alexander Armstrong

Doctor of Philosophy

Department of Sociology University of Toronto

2013 Abstract

Following Merton (1942) science studies has moved from the philosophy of science to a more sociologically minded analysis of scientific activity. This largely involves a shift away from questions that bear on the context of justification – a question of rationality and philosophy, to those that deal with the context of discovery. This thesis investigates changes in science studies in three papers: sociocultural evolutionary theories of scientific change; general trends in science studies - especially concerning the sociology of science; and a principle component analysis

(PCA) that details the development and interaction between research programmes in science studies. This thesis describes the proliferation of research programmes in science studies and uses evolutionary theory to make sense of the pattern of change.

Acknowledgments

I am deeply indebted to many people for supporting me during this journey and contributing to this thesis.

Throughout my graduate experience I have increasingly come to realize that mentorship is the single greatest factor that influences success. I am extremely grateful to my co-supervisor and mentor Professor Marion Blute for her faith in me and for sticking with me throughout my graduate career. There is no question that she saved my graduate career and for this I am grateful beyond words. Professor Blute has been the archtype of a mentor for me throughout the years. Her unwavering support and commitment helped me through many difficult times. She showed tremendous patience as I developed into a more mature, responsible student and her influence has set me on a path in academia that I am excited for.

I wish to thank Professor Zaheer Baber and Professor Bernd Baldus for their help throughout this process and for their contributions throughout my graduate experience. Between last minute meetings for comprehensive exams to working together in courses I am most grateful for their help and mentorship.

I owe a special thanks to the Department of Sociology at the University of Toronto for giving me the opportunity to conduct my doctoral research. The faculty and staff I have encountered along the way have been professional, courteous, helpful, and understanding. I have made many friends along the way and I thank them all for their friendship, patience, and their support as I struggled both professionally and personally through this sometimes difficult process.

Finally, it is difficult to express in words the feelings of love and gratitude I have for my parents and my brothers. Had it not been for their unconditional support I would not have had the confidence in myself to start on this path let alone persevere through the immense challenges I faced. I deeply regret not completing this thesis before my mother’s passing in July but she, and my father and brothers, was on my mind throughout my writing. Her strength and courage and the strength she brought out in all of us during her battle was my single greatest motivation to complete my research and it continues to motivate me as I start along a new path in life.

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Table of Contents

Abstract ...... ii

Acknowledgments ...... iii

List of Tables ...... vii

List of Figures ...... ix

Chapter 1 Introduction ...... 1

Chapter 2 Unraveling Scientific Development: Sociocultural Evolutionary Theories of Science as a Process...... 4

1 Evolutionary Theory and Cultural Change ...... 4

2 General Theories of Scientific Change ...... 4

3 Scientific Change: Variation, Selection, Reproduction, Pattern...... 5

4 Primary Considerations ...... 6

5 Evolutionary Theories of Science ...... 8

5.1 Thomas Kuhn ...... 8

5.2 Stephen Toulmin ...... 9

5.3 Karl Popper ...... 10

5.4 David Hull ...... 12

5.5 Marion Blute & Paul Armstrong ...... 13

6 Debates and Criticism ...... 14

7 Conclusion...... 15

Chapter 3 Reports of the Death of the Sociology of Science Have Been Greatly Exaggerated ... 16

8 Introduction: The legend ...... 16

9 Data source and methods ...... 20

10 Research questions and results ...... 22

10.1 Has the sociology of science become extinct? ...... 22

10.2 Given that it has not become extinct, has the “sociology of science” continued to be dominated by its parent, the “sociology of knowledge”, or come to be dominated by newer offshoots - for example the “sociology of scientific knowledge”, “social studies of science”, “social epistemology”, or “scientometrics”? ...... 23

10.3 Whatever the appropriate description of the level of institutionalization achieved (e.g. topic, field, research programme, paradigm, discipline etc.), does the sociology of knowledge, sociology of science and the newer enterprises constitute distinct ‘species’ in the sense that they are socially isolated from each other and fail to intercommunicate (in the way that members of different biological species fail to exchange genes or speakers of different languages fail to communicate with each other)? Or, on the other hand, are they varieties of the same species? ...... 24

11 Conclusion...... 26

Chapter 4 The Evolution of Research Programmes: An Author Co-citation Analysis of Science Studies, 1949-2011 ...... 34

12 Introduction ...... 34

13 Sociology of Science and Theories of Scientific Change ...... 34

14 Data Source and Methods ...... 37

15 Author Co-citation Analysis ...... 38

16 Selection of Authors ...... 39

17 Proximity Matrix, Factor Analysis ...... 40

18 Findings: 1964-1978 ...... 40

19 Findings: 1979-1993 ...... 45

20 Findings: 1994-2011 ...... 50

21 Discussion ...... 55

21.1 What is the level of substantive variation in science studies? ...... 55

21.2 How has this variation changed in time? ...... 55

21.3 What accounts for this change? ...... 56

22 Conclusion...... 58

Chapter 5 Species of Science Studies ...... 64

23 Numerical Taxonomy ...... 64

24 Evolutionary Taxonomy ...... 65

25 Cladistic Analysis...... 65

References ...... 66

List of Tables

Table 1: Correlation of Publication Counts

Table 1.1: Selection of Authors to Include in Factor Analysis: Mean citation count of authors by research programme and time period.

Table 2: The Main Ideas for Each Component by Time Period

Table 2.1: Total Variance of Author Co-Citation Counts by Extracted Factors: 1964-1978

Table 2.2: Correlation between Authors and Components: 1964-1978

Table 2.3: The Correlation between Components: 1964-1978

Table 3.1: Total Variance of Author Co-Citation Counts by Extracted Factors: 1979-1993

Table 3.2: Correlation between Authors and Components: 1979-1993

Table 3.3: The Correlation between Components: 1979-1999

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Table 4.1: Research Programmes and Authors Included in Component 1: 1994-2011

Table 4.2: Total Variance of Author Co-Citation Counts by Extracted Factors: 1994-2011

Table 4.3: Correlation between Authors and Components: 1994-2011

Table 4.4: The Correlation between Components: 1994-2011

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List of Figures

Figure 1: Sociology of Science Counts

Figure 2: Science Studies Publications – Proportionally

Figure 3: Proportion of More Constructionist/Non-Constructionist Publications

Figure 4: Proportion of Articles Published in Sociology or More Constructionist Journals

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Chapter 1 Introduction

Science studies have undergone many iterations since the publication of Robert Merton’s path breaking ‘The Normative Structure of Science’ (1942). He demonstrated that, like any other institution, the direction science takes is enabled and constrained by socio-structural factors and institutional norms. Merton’s (1937, 1942) functionalist sociology of science introduces communalism, universalism, distinterestness, originality, and skepticism into science studies. These norms are used to explain the institutional characteristics of science and how they interact with variables like status to explain scientific change. Merton’s analysis effectively shifts the discourse from the philosophy of science to one that is sociologically minded. With it science studies move away from questions that bear on the “context of justification” – a question of rationality and philosophy, to those that deal with the “context of discovery” (Leydesdorff, 1989; Dolby, 1971; Hess, 1997; Armstrong & Blute, 2010). In this intellectual context studies of science broadly view science as socio-cultural practice (Callon, 2001). The strong program of the sociology of scientific knowledge (SSK) is an area of research broadly characterized by constructionism in its approach to sociological knowledge and reflects the dissolution of the distinction between discovery and justification (Bloor, 1976; Hess, 1997; Armstrong & Blute, 2010).1 Hess’s “where the field is moving” coincides with Sismondo’s (2008) and Yearley‘s (2005) account and brings the area to its current point. It broadly features cultural studies of science, actor-network theory, ethnomethodology, and what Callon (2001) terms “extended translation.”

In the following three papers I investigate these changes in three ways: sociocultural evolutionary theories of scientific change; general trends in science studies - especially

1 Kuhn’s (1962) ‘The Structure of Scientific Revolutions’ also dissolves the boundary between discovery and justification as unique analytical components of science. Kuhn wants to overcome the shortcomings of the two prominent programmes at the time of his writing: sociological and scientometric approaches. I will elaborate on scientometrics in the second paper.

2 concerning the sociology of science; and a principle component analysis (PCA) that details the development and interaction between research programs in science studies.

‘Unraveling Scientific Development: Sociocultural Evolutionary Theories of Science as a Process’ is my argument for using evolutionary theory to pattern developments in science studies. In this paper I look to Popper, Kuhn, Hull, Toulmin, and Blute & Armstrong for exemplars of evolutionary theories of scientific change. Although each author relies on evolutionary theory to a varying degree, evolutionary theory is well-suited to explain change in science and the mechanism by which it occurs: descent with modification and natural selection.

‘Reports of the Death of the Sociology of Science Have Been Greatly Exaggerated’ is a quantitative investigation of the output of the sociology of science and its relationship with other research programmes in science studies. Recent statements about the decline of the programme motivate the paper but conversely I find that the sociology of science is not in decline or being outcompeted in the science studies environment. Though the environment is growing – exemplified by the emergence of new research programmes - the sociology of science is maintaining its distinctiveness.

‘The Evolution of Research Programmes: An Author Co-citation Analysis of Science Studies, 1949-2011’ is a principle component analysis of the structure of science studies using co-citation data of the major authors in the field. I detail the nature of each component that is derived for three time periods and explain the patterns of interaction between the groupings of authors. There are two findings of interest: 1) methodological and 2) substantive. Methodologically I find that author co-citation analysis from journal articles produces a “messy” portrait of science studies. Research programmes – or authors’ self-use of these labels - does not accurately map the substantive composition of the field. Substantively I find that the environment and the density of authors/programmes in these environments is increasing through time. The nature of the diversification of science studies is also characterized by conflict, competition, and cooperation

3 and each is evident in the substantive contents of each component. I synthesize this evidence in conjunction with the findings of the previous papers using evolutionary theory and I recommend future areas of research.

Chapter 2 Unraveling Scientific Development: Sociocultural Evolutionary Theories of Science as a Process. 1 Evolutionary Theory and Cultural Change

The application of evolutionary theory to sociocultural topics is not new. Baldus, for example, uses evolutionary theory to explain the emergence and maintenance of social inequality (Personal Communication, 2011). Similarly, Currie et. al (2010) employ phylogenetic methods to trace changes in political complexity in South-East Asia and the Pacific. They find that a sociocultural evolutionary approach to the development of hierarchical political organizations is supported by statistical data. Basalla (1988) offers an evolutionary account of technological development (artifacts) that focuses on the selection of traits based on their different fitness relative to political, economic, military, etc. conditions. Aspects of Darwin’s theory have also influenced social theory. For example, Karl Marx is said to have modeled his stage-theory of historical development after aspects of Darwin’s evolutionary theory. Similarly, Durkheim’s shift from mechanical to organic solidarity contains a natural progression in the form and complexity of society (Toulmin, 1972). In a 1999 article, Hussey argues that aspects of evolutionary epistemology and evolutionary theories of science reflect fundamental misunderstandings of Darwin’s theory. In this paper I review evolutionary accounts of the development of science/scientific knowledge and assess their strengths. A review of research exemplars proceeds according to how explicitly they rely on evolutionary theory, from most basic to the most explicit. I conclude by briefly situating these theories in the broader theoretical literature of sociocultural evolutionary theory.

2 General Theories of Scientific Change

The study of science, scientists, and scientific activity proceeds along an interesting trajectory due to the nature of the phenomena under study. It is said that reality is an ‘external’ reality

5 waiting to be discovered and scientists are said to report observations of this objective reality (Giere, 2006). Toulmin states: “the business of science (it was thought) is to study the causes of natural phenomena; whereas science itself, as a rational activity, presumably operated on a higher level, and could not be thought of as a “natural phenomenon” (1967, p.456). This conception of scientific knowledge as somehow unique or different is increasingly challenged by various theoretical developments in science studies. Price’s (1963) groundbreaking statistical analysis of scholarly output brings scientific activity itself into the purview of analysis and frames it as an output of human activity. Kuhn’s (1962) ‘revolutions in science’ identifies paradigms (and paradigm shifts) as central analytic concepts while Merton’s (1937, 1942) functionalist sociology explains science as a social institution and introduces the norms of communalism, universalism, distinterestness, originality, and skepticism into science studies. Woolgar and Latour (1979), in their laboratory studies, argue that external reality is constituted by the tools we use to study it and it does not exist objectively but as objects of our contemplation (1979). Shapin (1975), Collins (1993), Pickering (1995), and Giere (2006) all argue that group interests, methodology, the research process, and instrumentation affect scientific results and knowledge claims. Hess’ so-called third phase marks the dissolution of the theoretical boundary between ‘discovery’ and ‘justification’ and the movement towards sociological explanations of sociological knowledge itself (SSK), thus moving past the Mertonian-Kuhnian approach towards a more “constructionist” approach (Hess, 1997; Armstrong and Blute, 2010). More generally, Woolgar and Latour’s (1979) more general point is that scientific facts are entities that are created, not simply observed. In terms of theorizing science, so-called constructionist approaches generally hold grand narratives in disdain. However Blute and Armstrong (2011) identify grand theories of science/scholarship offered by ten contemporary sociologists or sociologically-minded philosophers and use interviews and textual analysis to elaborate their similarities and differences on ten issues. One such approach is evolutionary theory.

3 Scientific Change: Variation, Selection, Reproduction, Pattern.

General theories of science conceptualize change in various forms, some of which resemble those in evolutionary theory. Bunge (2003) makes the case for mergers in science among different disciplines and research programmes. These mergers resemble hybridization in evolutionary theory. Drori’s (2003) neo-institutional approach is a sort of evolutionary ecological approach whereby institutions spread globally and each interacts with a slightly different environment and takes on a ‘glocal’ form. Abbott (2001) finds a similar branching-type development however he argues that a self-repeating cleaving or conflict results in a fractal pattern. This type of branching is also shared by evolutionary theories of science however it differs in key respects.

4 Primary Considerations

The evolutionary theorist Ernst Mayr (1988) argues that, contra Plato (and later Aristotle), species cannot be expressed in nomothetic, necessary and sufficient terms. To do so is what he calls ‘typological essentialism’. Instead he puts forth the notion of ‘populational thinking’: species have many things in common but not necessarily everything (they are polythetic). Proceeding further, for Hull (1988) and Ghiselin (1975) biological species are individuals (not to be confused with organismic individuals). These individuals/entities are located in a specific time and space. Popper, Kuhn, and Toulmin share this idiographic approach to species - the thing that they describe (theories, scientific concepts) does not have an inner nature, an unchanging nature, or an innate nature. These theorists take an anti-essentialist approach in their views of scientific development.

Karl Popper (1959) (1972) famously set out to solve the ‘problem of induction’ and argued that theory always precedes observation in the formation of knowledge. Like Hume, Popper believes that it is a fallacy to establish natural laws (true theories) on the basis of repeated observation. However, Hume proceeds further and argues that repetition is not a reasonable basis for knowledge and yet it is a primary mechanism for our experience and knowledge of the world

(custom or habit). “Even our intellect does not work rationally. Habit, which is rationally indefensible, is the main force that guides our thoughts and our actions” (Hume, as cited in Popper, 1997, p.95). For Hume human knowledge is irrational because rational people believe in the validity of induction. This psychological aspect of the problem of induction is where Popper breaks from Hume. Instead Popper seeks to accept the logical problem of induction while maintaining human rationality (a psychological aspect). He performs this feat first by separating the logical and the psychological aspects of the problem of induction. The central motivating force in the development of knowledge is rational: criticism/dissatisfaction among practitioners of existing theories. We reason rationally and act accordingly because our theories are based not on induction but in accordance with reason. Justifying existing theories cannot lead to true knowledge and instead science should strive to create theories that are falsifiable. Knowledge starts from problems and develops because under scrutiny existing theories are bettered by new theories that have a higher degree of precision and testability. We have rational reason to believe our theories are true in a context of competing theories (Popper, 1972).

Kuhn (1962) largely agrees with these epistemic foundations and argues that observation is not the basis of knowledge because both observation and measurement are conditioned by existing standards and norms. Research is bound by tradition and more specifically it is bound by tools and conceptual resources inherited from past generations. However, the act of discovery, for Kuhn, is not exclusively conditioned by “external” factors. Epistemic factors form the basis of scientific change and supersede the external conditions suggested by sociologists like Collins and Ben-David (who focus on the effects of professions) and Price (concerned with the quantity of scholarly output) (Wray, 2011).

Toulmin (1967) shares this assumption - exemplified in his desire to “show how a history of ideas is related to a history of people” (p.459). What he means is that scientific development has continuity both intellectually and in institutions. Whereas Kuhn and Popper are interested in describing change according philosophical (read: logical) criteria, Toulmin situates the production and selection of variation within the social sphere. “Scientists commonly take it for granted that their criteria of “truth,” “verification,” or “falsification” are stateable in absolute

8 terms” (Toulmin, 1967, p.463). The implication is that the system that produces and selects among variations is socio-historical and a product of human activities. For Popper the historical development occurs in accordance with logical principles (conjectures and refutation) (Toulmin, 1967; Devettere, 1973). These are the theoretical and logical bases on which each theorist proceeds with their evolutionary theory of science.

5 Evolutionary Theories of Science

Evolution involves the selection of traits within a population that are recreated within a lineage. A lineage resembles a tree with entities that have descended, with modification, from a common ancestor(s) (Blute, 1997). Toulmin (1967, 1972) argues that Darwin’s ‘variation and natural selection’ is in fact a general historical explanation that can be applied to other historical entities, including science. Popper agrees and states “all this may be expressed by saying that the growth of our knowledge is the result of a process closely resembling what Darwin called ‘natural selection’; that is, the natural selection of hypotheses” (1979, p,261). One of the key components of selectionist or evolutionary theory is random or blindly occurring variation in genetic material every generation. In Darwin’s theory variation occurs prior to selection and is attributed to mutation and recombination (Blute, 2010). These concepts have analogous ones in sociocultural evolutionary theory. Kuhn and Popper appear to employ these evolutionary concepts in a way that is more of an analogy whereas Toulmin, Hull, and Blute & Armstrong offer the most ‘faithful’ evolutionary accounts of the development of science.

5.1 Thomas Kuhn

Toward the end of The Structure of Scientific Revolutions (1970) Thomas Kuhn argues that scientific knowledge develops in a fashion similar to how organic species evolve. His main emphasis here though is epistemological as he seeks to take a developmental view of science

9 whereby nature (and ultimate knowledge of it) is replaced as the chief aim of science (Wray, 2011). Instead science is driven from behind and not toward any fixed, external goal. More precise conceptual tools continually replace old theories and tools as science becomes increasingly specialized (Kuhn, 1970; Wray, 2011). As science develops its practitioners narrow their scope which results in a branching pattern of an increasingly detailed conception of nature. For Kuhn Darwin’s theory of natural selection and descent with modification is especially relevant because Darwin sees a greater variety of species evolve. This is akin to Kuhn’s scientific specialties. Furthermore, there exists a degree of continuity in the process of discovery as both the problems scientists encounter and the conceptual tools they employ are previously developed and inherited from predecessors. And like Darwin’s Principle of Divergence (Mayr, 1992) , Kuhn remarks that increasing specialization in science tends to restrict the interaction and communication between different specialties as the refinement of their hypotheses, methods, and tools has left them with less and less in common (1970).

5.2 Stephen Toulmin

Stephen Toulmin (1972) takes a populational approach to scientific change and uses a selection- process to reconcile internalist and extrernalist theories. Rather than think of science in either-or terms, Toulmin argues that the evolution of conceptual populations reflects a double-edged process involving innovative factors (these represent variations) and selective factors (that perpetuate favoured variants). He outlines four Darwinian commonplaces that are applicable to conceptual development: 1) intellectual enterprises fall into ‘disciplines’ that contain their own methodologies, concepts, and fundamental aims; 2) intellectual innovation is a continual process that depends on (and is balanced by) critical selection; 3) ‘Forums of competition’ provide the ecological conditions within which variations demonstrate their fitness; 4) “in any problem situation the disciplinary selection process picks out for ‘accreditation’ those of the ‘competing’ novelties which best meet the specific ‘demands’ of the local ‘intellectual environment’” (Toulmin, 1972, p.140; Nowotny, 1974). Selection is a communal affair that depends on a common identification and agreement of the worthiness of novel suggestions to address historically situated problems/puzzles. The notions of “testing”, “proving”, and “falsifying” are

10 all intellectual goals of scientific disciplines within which the criteria of selection operate and these goals are constituted in historical terms. Ideas are selected within disciplines if they meet the demands of the situation (the external environment) better than predecessors. Conceptual disciplines are historical entities characterized by growth and by intellectual and institutional continuity. Given the interaction between variants and their local environment, the volume of intellectual innovations largely reflects external forces. Mutation frequency directly links conceptual development with organic development (Toulmin, 1972). Selection provides the basis against which new innovations are measured and, if accepted, incorporated into the conceptual ‘gene pool’ to be ‘taken up’ by the younger generation of scholars in the master-pupil relationship. Importantly, the vision of nature of each generation is never replicated exactly as Toulmin argues this would be the sign of scholasticism. Instead, each generation re-creates their vision of nature by combining the ideas they have encountered historically with the newly incorporated variation.

Later intellectual cross-sections of a tradition reproduce the content of their immediate predecessors, as modified by those particular intellectual novelties which were selected out in the meanwhile – in the light of the professional standards of the science of the time (Toulmin, 1967, p.466).

Toulmin invokes additional nomenclature from organic evolution in the case of new specialties arising that have a unique history and genealogy (hybridization and cross-fertilization) and he points to Abbott’s fractal model as a potential need to refine the evolutionary model. Interestingly, Bunge concedes that patterns other than mergers are possible including specialization and convergence and he compares these processes to biological hybridization (as cited in Blute & Armstrong, 2011).

5.3 Karl Popper

Popper is known for his philosophical approach to scientific development that is based on conjectures and refutations. Science proceeds, according to Popper, not by attempts to support and justify existing theories. Scientists create theories that are testable and refutable and when tested and refuted results in theories of more accurate empirical content (Lakatos, 1968; Popper, 1979). Theories, in their development, assume a greater degree of universality and better solutions to new problems. This improvement of new hypotheses over old ones is analogous to comparative relative fitness (the chosen learning mechanism), for Popper. “The fittest hypothesis is the one which best solves the problem it was designed to solve, and which resists criticism better than competing hypotheses” (Popper, 1979, p.264). It is systematic criticism that determines what theories are unfit and hence eliminated (Popper, 1979). Systematic criticism goes hand in hand with trial and error learning and provides an opportunity to explain creative thought in a non-teleological, deterministic fashion (Campbell, 1988; Popper, 1979). The pattern of evolution varies according to the nature of science being considered. Applied knowledge and human technology takes a form similar to organic evolution: increased branching through time corresponding to increasingly specialized differentiated hypotheses (Popper, 1979). However, the branching pattern of pure knowledge is different from organic evolution. Despite the differentiation of problems pure knowledge tends to integrate into more general theories with greater explanatory power. Popper argues that, unlike organic evolution, pure knowledge is like a series of disconnected branches that converge over time. This tendency, he argues, stems from the aim of scientists to develop better explanatory theories and to employ rational criticism to find better theories. True theories here means they serve a better role in criticism and eliminating unfit explanations (Popper, 1979). The difference in the direction of branching is based on the level of interaction between hypotheses in scientific fields. This pattern is also evident in organic evolution. Blute (2010) states, “branchings and mergers are about the absence or presence of sexual interaction while diversification and convergence are about the absence or presence of similarity” (p.42). For Popper, mergers occur when there is more social interaction between practitioners and branching results when hypotheses are differentiated (read: they no longer interact).

5.4 David Hull

David Hull (1988) proposes an explicitly evolutionary theory of scientific change that encompasses branching (Abbott, 2001; Drori, 2003), stability (Collins, 1998), convergence (Bunge, 2003) and linear development (Ziman, 2000; Fuller, 2006). Hull seeks to build upon his conceptualization of the selection process as a function of both interactors and replicators. A replicator is defined as “an entity that passes on its structure largely intact in successive replications” (Hull, 1988, p.408). An interactor is defined as “an entity that interacts as a cohesive whole with its environment in such a way that this interaction causes replication to be differential” (Hull, 1988, p. 408). Lastly, replicators and intercators function together in selection: “a process in which the differential extinction and proliferation of interactors cause the differential perpetuation of the relevant replicators” (Hull, 1998, p. 409). With these definitions in mind Hull is able to analyze the structure of science and scientific ideas as a selection process.

For Hull, the substantive elements that exist in science function as replicators in conceptual change. Thus, everything from beliefs about the goals of science to the appropriate ways to go about realizing those goals is a replicator. Scientists share ideas that are identical by descent and then are often recombined. This is facilitated by the coincidence of the scientists’ career goals with the manifest goals of scientists. In this respect, the desire to gain credit among the scientific community is a prime mechanism in conceptual selection. Credit is a key aspect of the reward structure that scientists are embedded in and interacts with a system of trust and cooperation to produce the distinct feature of science (Godfrey-Smith, 2010). This leads to concurrent mechanisms including testing/checking and curiosity (Hull, 1988). Checking is a mechanism that results from the fact that all publications (a vehicle) are based to some extent on previously existing knowledge. Thus all scientific findings must be testable (but not necessarily tested). Throughout the credit/checking process, ideas are subject to competition and relate to each other as lineages. In this case, lineages may be conceived of as research programmes/theories – all entities that are historical and located temporally and spatially. The scientists that interact within these lineages may function as vehicles for replicators and intercators.

Hull offers several significant theoretical insights that should be considered when studying conceptual change. The first is the recognition that conceptual change is necessarily linked to individuals that are historically situated, and thus the concepts may be analyzed as individuals. This implies that any analysis of conceptual development in science must consider the role that research groups play in influencing scientific ideas, thus identifying a social aspect of scientific change. The second is Hull’s separation of selection into two component parts; interaction and replication. This distinction ensures that conceptual selection will not be limited to preconceived notions of replicator, interactor, and selection. This is exemplified in his example of a gene that both replicates its genetic code and similarly interacts with its environment as a cohesive whole. These key features of selection are then applied to conceptual change in science as Hull seeks to identify interactors, replicators, and lineages in science. Lastly, Hull implores the reader to accept his argument that empirical research and testing of hypothesis are critical components of the evolution of scientific change. This includes replacing thought experiments with real examples and introducing stronger methodological components.

5.5 Marion Blute & Paul Armstrong

Blute & Armstrong (2011) find that general theories of science/scholarship conceptualize change in many patterns including branching, linear, merging, and converging. They argue that all of the useful ideas in grand theories of scientific change can be incorporated in a Darwinian sociocultural evolutionary theory. Evolution’s descent with modification handles all of the patterns of change and novelty and repetition (including cyclical). Selection is a universal mechanism that explains why, in any given context, certain ideas, theories, research programmes, or methods spread or do not. Evolutionary theory also incorporates all of competition, cooperation and conflict.

In science, new environments both internal and external to the institution itself can restructure old ideas, new ideas can reconstruct old environments, and sometimes both can even occur simultaneously so that they mutually structure and construct each other - interact more or less as Latour has it (Blute & Armstrong, 2011, p.422-423).

6 Debates and Criticism

Toulmin’s model shares many features with other thinkers though he tends not to agree entirely with the accounts given by any. For example, Popper’s ‘conjectures and refutations’ (the freedom of conjecture and the severity of criticism) are critical, says Toulmin, for “enhancing the pool of conceptual variants” and “enhancing the degree of selective pressure” (1967, p.471). Toulmin agrees with Popper that new variants are selected for incorporation in disciplines if they have greater explanatory power than existing concepts. However, they differ fundamentally on the philosophical basis of the standard by which a concept is deemed to be ‘better’. Toulmin (1972), by locating the selection-criteria within scientific institutions, decouples rationality from the realm of philosophy.

We must begin, therefore, by recognizing that rationality is an attribute, not of logical or conceptual systems as such, but of the human activities or enterprises of which particular sets of concepts are the temporary cross-sections: specifically, of the procedures by which the concepts, judgments, and formal systems currently accepted in those enterprises are criticized and changed (Toulmin, 1972, p.133).

By maintaining that rationality is part of a logical system Popper is employing a kind of evolution geared toward some end goal. The notion of an ‘end goal’ is problematic in the realm of sociocultural evolution because it suggests that selection occurs directly and is thus Lamarckian (Hussey, 1999). Although mechanisms for the inheritance of acquired traits have

15 been theorized, it is largely accepted that adaptations must be heritable to be used in biological evolution. However this is one of the general criticisms leveled against evolutionary theories of science: the origin of variation in the sociocultural realm is not random or blind (Hussey, 1999; Bryant, 2004). Bryant suggests that the biological account is flawed when it comes to culture because “the socially constructed worlds of human agents constitute ‘environments’ of a rather different order than the physical natural world that serves as the arena for biological struggle and evolution” (2007, p.463). Therefore variation, fitness, and adaptability are all socially conditioned entities (Bryant, 2004). In the realm of science, it is argued, innovations arise due to pressures of the environment and they are designed purposely to avoid criticisms. Unlike a Darwinian model, there is a sort of pre-selection in science that directs scientists’ activities towards a presupposed goal (Hussey, 1999). This, Hussey argues, is precisely why a Lamarckian process of evolutionary change is more appropriate when describing science than a Darwinian one. The demands of the scientific environment direct variations and the mechanism for producing change is direct and involves judgments about that same environment. However evolutionary theory is historical. This is, as I’ve noted, perhaps the main underlying reason why Kuhn and Popper are drawn to it. As such it incorporates historical contextual factors as evidenced in Toulmin’s attempt to relate “a history of ideas to a history of people” (1972, p.459). Second, evolutionary theory seeks to explain scientific development through evolutionary processes that are historical, path dependent, and concern both the lived realm and inheritance. This partially alleviates Bryant’s concern that: “it [biological colonization of the sociological disciplines] through reductionist accounts [seeks to] explicate social phenomena as direct phenotypical expressions of underlying biological or genetic factors” (2004, p.460).

7 Conclusion

I have demonstrated how a novel account of evolutionary theory makes sense of the development of science. So long as an evolutionary account incorporates an historical analysis of historically situated pathways, it transcends the boundaries between historical narrative and deterministic causal theories.

Chapter 3 Reports of the Death of the Sociology of Science Have Been Greatly Exaggerated

8 Introduction: The legend

This title echoes Mark Twain who is apocryphally reported to have said that “the reports of my death are greatly exaggerated” after his obituary was published in the New York Journal in 1897 while he was still very much alive. He died in 1910. His actual words “the report of my death was an exaggeration” convey the famous sentiment, albeit a little less memorably1.

Here we report on another death which has been greatly exaggerated - that of the sociology of science. According to a widespread legend, the sociology of science became extinct and was replaced by one or more new modes of observing and theorizing about science. At the very least, these new models are viewed as having been added to the mix and having come to dominate (e.g. see discussion of Hess 1997, Yearley 2005, Sismondo 2008, and Restivo and Croissant 2008 below). The candidates usually mentioned are the sociology of scientific knowledge (SSK), social studies of science, or social epistemology. Such legends are so widely believed in the field most inclusively known as science studies2 that a prominent philosopher of science issued a plaintive call for a revival of the sociology of science (Kitcher 2000). Similarly, Frickel and Moore (2006) collected a series of case studies which they view as representing a revival of the sociology of science, but one centered on the political. A related possibility is that the different modes of studying science have become wholly distinct. Hull (2000) viewed this as undesirable and called for more interaction, to be achieved by “cutting each other some slack”, an attitude he viewed as prevailing in the study of Biology where philosophers, historians, social scientists and even biologists interact in the International Society for the History, Philosophy and Social Studies of Biology. So - it should be revived, it is being revived, it could be revived, but perhaps first the question should be asked whether it ever died. Before some data is presented, an extremely abbreviated history will be useful.

Originally, the philosophy, history, and sociology of science were independently institutionalized (Hull 2000). Although metaphysics had long since evolved into the natural sciences and epistemology into psychology and the social sciences, the philosophy of science for long remained curiously immune to this ‘scientizing’ of what were once exclusively philosophical topics. Eventually however, the reigning paradigm in the philosophy of science in the latter part of the first half of the twentieth century, logical positivism, melted under Quine’s (1951, 1960) attack on the analytic-synthetic distinction and his embrace of a (psychological) “naturalized epistemology” (1969) as well as Kuhn’s (1962) historical account of revolutions in science. These led ultimately to the conclusion that only theories as a whole have empirical import and they, or more inclusively, paradigms or research programmes as a whole, only relatively, i.e. in competition with others. In the view of some, these developments in philosophy led naturally not so much to Popper’s (1959, 1962, 1972) falsificationism, his “conjectures and refutations” emphasizing only selection against, but rather to “conjectures” and changes in relative frequencies by means of any, or all of, competition, conflict and cooperation - i.e. to evolutionary theories of scientific change such as those of Toulmin (1972) and Hull (1988). As a minimum, they helped make space for the professionalization and institutionalization of the history and sociology of science.

Some of Robert K. Merton’s writings on the subject of science date to the 1930's and 40's in which he made it clear that he viewed the sociology of science as a branch of the sociology of knowledge (e.g. Merton 1937) pioneered by Karl Mannheim. However, the sociology of science was not fully institutionalized until the 1960's - primarily at Columbia University by Merton, but also at The University of Wisconsin at Madison by Warren O. Hagstrom, and briefly at the University of California at Berkeley by Joseph Ben-David (long associated with the Hebrew University at Jerusalem). In addition to their own work (e.g. Hagstrom 1965; Merton 1973; Ben- David 1971), each produced influential students (e.g. Lowell Hargens; Bernard Barber, Jonathan Cole, Steven Cole, Harriet Zuckerman; and Randall Collins respectively). Although Merton’s general thesis that scientists compete for status (“recognition”) rather than income, wealth, or power, and his earlier articulation of the norms of the scientific community are the most widely

18 cited and quoted ideas from this body of work, the topic most extensively studied by this ‘school’ (in keeping with sociology at large of the time) was the determinants of social stratification and mobility in science. What matters most for success - productivity, prestige of the university of Ph.D. or status of mentor for example? The formal and informal organization of the scientific community also received a great deal of attention. (On this general history see Storer’s introduction to Merton 1973; Ben-David and Sullivan 1975; Cole 1992; Hess 1997: Chpt. 3)

David Hess, who published one of, if not the first, pluridisciplinary text on science studies in 1997, laid out the historical narrative of science studies this way. He viewed the field as having gone from i) the philosophy of science, to ii) Merton’s institutional sociology of science, to iii) the strong programme of the sociology of scientific knowledge (SSK) which coalesced around Edinburgh and Bath in the United Kingdom in the 1970's (e.g. David Bloor, Michael Mulkay, Harry Collins, Barry Barnes) and finally to iv) “where the field is moving” - broadly labelled as “critical and cultural studies of science” - including “anthropology, critical social theory, cultural studies, feminist studies, critical technology studies, and the cultural history of science” (1997: 3). It is worth noting that, while Hess adopted the convention of labelling the earlier Mertonian- style work as a different species so to speak, “the institutional sociology of science” rather than just “the sociology of science”, he was among those who saw the new work as additions that have come to dominate rather than to replace the older work (e.g. p. 84).

Moving to a more recent text (Yearley 2005) and very recent reviews (Sismondo 2008; Restivo & Croissant 2008) we find some changes. For example, the earlier philosophy of science and Merton’s “institutional” sociology of science tend to be more or less dropped from the narratives. Hess’s account of where the subject was going in 1971 has become more differentiated. For example Yearley identifies three “schools” in addition to the original Edinburgh one - Latour’s actor-network theory; the study of gender and science; and ethnomethodology and discourse analysis. Sismondo sees laboratory studies in general (including Latour and Woolgar 1979) and ethnomethodology (e.g. Lynch 1985) as having succeeded the Edinburgh-Bath school, but sees the main division currently (after Fuller) as being between a “high church” focused on science

19 and a more activist “low church” interested in technology and in making the latter accountable to the public interest.

One of the things that Hess originally, and most later historians and reviewers of the changes have agreed on, is that post-Mertonian science studies became “constructionist” under the influence of Bloor’s strong programme (and perhaps also under Collins 1985 “empirical programme of relativism”). Feyerabend (1975, 1978) probably deserves more credit for the change than he is usually given, perhaps because his “anarchic” views eventually became an embarrassment to the field. In any event, Bloor (1976) is credited with making the first move by arguing for a “strong programme” that, after Mannheim, again addressed the knowledge content of science. (He took aim at Merton for being insufficiently knowledge-focused and excessively institutionally-focused). The strong programme would be causal, impartial with respect to the truth or falsity of a belief i.e be symmetrical in its explanation of both, and reflexive i.e. applicable to itself. According to Yearley (2005: Chpt. 2), Bloor is the “symbolic heart”, and Bloor and Collins laid down the “framing commitments” of what followed. The constructionist metaphor was ubiquitous in the 1980s and 1990s according to Sismondo (2008: 14) and is the “fundamental theorem” of the subject according to Restivo and Croissant (2008: 214).3

One thing these authors (and others) do not agree on is whether or not the post-Mertonian research under discussion is sociology. Sismondo (2008) hardly mentions the word. At the opposite extreme, Restivo and Croissant (2008) have no doubt that it is. They go so far as to view social constructionism as the ultimate realization of the nineteenth century theories of Durkheim, Marx, Weber, Nietzsche and Simmel among others (214). In between, Yearley (2005) is obviously ambivalent. On the one hand Sociology does not appear in his title: Making Sense of Science: Understanding the Social Study of Science. On the other hand, the text claims that the book is “primarily for the benefit of a sociological audience” and states that its purpose is to “ investigate and remedy the disregard for the sociology of science in social theory” (xiv). This ambivalence is sometimes poignantly expressed, “Sociologists . . . they (or rather, we). . .” (2005: 62). One thing that is clear however, is that as the “constructionist” theme took hold, labels other than the sociology of knowledge and sociology of science appeared and began to

20 become common - not only the “sociology of scientific knowledge” but also slightly less frequently “social studies of science” and “social epistemology”.

If these accounts tend to agree that the field became “constructionist” but disagree on whether these new strains of science studies are in fact sociology, another feature that they all have in common is a curious omission of any discussion or even mention of “scientometrics” (sometimes but less commonly called the “science of science”). As Merton (2000) noted in his essay in the Festschrift in honour of Eugene Garfield, while the Science Citation Index was designed as a bibliographic retrieval system for science itself, Garfield quickly recognized that he had invented a specialty-specific research tool in the sociology of science, one which Merton’s students quickly began to make use of. Indeed, much of the research performed in the sociology of science would have been impossible without it. An unusual fact about science studies is that the mainstream journals (e.g. Social Studies of Science, Science Technology & Society, Social Epistemology, Episteme: A Journal of Social Epistemology, and Science, Technology & Human Values - the latter the official journal of the Society for Social Studies of Science) tend to be dominated by qualitative research with the quantitative largely confined to its own journal, Scientometrics - the reverse of the situation that tends to prevail in Sociology more generally. However, there is no doubt that even a cursory inspection of Scientometrics founded in 1978 reveals that the bulk of research published there is sociology of science by any standard. For example a recent issue (V79 # 3, June 2009) includes articles on the influence of a particular author, the social links between two kinds of scientific organizations, gender differences in research productivity, whether China is becoming a power in the social sciences and the influence individuals have on the impact of the organization of which they are a part.

9 Data source and methods

In the light of the foregoing, we decided to ask some simple questions and methodologically, to investigate the ‘new’ interdisciplinary study of science with the kinds of bibliometric data and

21 methods characteristic of the ‘old’. Data was collected from the Web of Science yearly from 1957 to 2007 on items including the following expressions in their titles, abstracts or key words: “sociology of science”; “sociology of knowledge”; “sociology of scientific knowledge”; “social studies of science”; “social epistemology”; and “scientometrics”4. The Web of Science is not a perfect indicator of what academics are up to. It no longer includes books for example – but it is the best source of quantitative data available. Moreover, there is no guarantee that individuals all mean exactly the same thing in using one of these expressions. The idiolects of individuals are each a little different, usages in different subcultures somewhat more so, and in dialects even more so. However, if there were not at least statistical commonalities in linguistic reference, no communication would take place. Academics might as well stop writing. Indeed, humans would have never have invented language in the first place. Academics generally use keywords in particular to express how they construct what they are doing and to signal such to others, thus hoping to attract suitable readers.

Data for this project was collected from the Web of Science: a subsidiary of ISI Web of Knowledge. The database was accessed online through our library where we searched three citation indices: Science Citation Index Expanded (SCI-EXPANDED)--1900-present, the Social Sciences Citation Index (SSCI)--1956-present, and the Arts & Humanities Citation Index (A&HCI)--1975-present. Using the advanced search option, we employed the following search string: TS=”name of research programme” OR TI=”name of research programme”. The most significant details are that TS is an operator for topic (i.e. key words or abstract) while TI is the operator for title. The use of quotation marks around the search terms returns only results that use those exact words, and the OR statement searches for results that satisfy either condition. Furthermore, the TI search string searches for the words as they appear within a publication title, not necessarily the only words in the title. We also omitted articles from 2008 and 2009.

For social epistemology and scientometrics, we sorted the results by date and manually recorded the publication counts by year. For the remaining research programmes, additional steps were required. The significance of the word ‘of’ in their titles (sociology OF science, social studies OF science, sociology OF scientific knowledge, and Sociology OF Knowledge) caused some

22 complications in the search process. The Web of Science will not, even if included within quotation marks, limit its findings to exact phrases. Thus, a search for “sociology of scientific knowledge” would also include sociology of knowledge in its results. In order to obtain only those publications that contained the exact phrase, we exported the ‘full record’ of each record into an HTML file that was then searched for the desired phrase as an exact phrase and counted and then recorded, according to year.

To evaluate the overlap among expressions, we searched the HTML output of the “sociology of science”. The first step was to search, and highlight, all instances of the sociology of science. We then searched the document again, this time looking for each of the other research programmes. To be counted, the publication record had to contain both “the sociology of science” and the research programme in question. Publications that met this criterion were recorded manually by year. The database was then searched again for each of the remaining programmes (individually) and recorded accordingly.

10 Research questions and results

The questions asked and results are as follows:

10.1 Has the sociology of science become extinct?

The answer is a definitive no. With a fair amount of variability but an upward trend, the number of papers in the Sociology of Science has increased from a single one in 1957 to a yearly count ranging from 12 to 28 in each of the past five years (see Figure 1.)

The answer is a definitive yes for the former and a definitive no for the first three of the latter. From the first instance of the sociology of science in 1957 publications about the sociology of knowledge have outnumbered the former in 38 years and more generally by 210 publications (470 to 680). However, the number of publications in the sociology of science has exceeded those of the sociology of scientific knowledge, social studies of science, and social epistemology in every year from 1957 to 2007 except for 8 years for the first two and 6 years for the third in which they were equal, with the bulk of those being zeros for both in some early years. As well, the overall totals for all years are 470 for the sociology of science, 98 for the sociology of scientific knowledge, 93 for social studies of science and 87 for social epistemology. The sociology of science has also dominated scientometrics in all but 7 years (overall totals 470 to 275). On the other hand none of those are paired 0's, and moreover 4 of the 7 years have been in the last 5. This is suggestive of what may be a hint of the beginning of a trend for scientometrics to displace the sociology of science.

Of course, none of these facts should be taken to imply that the field has not changed since Merton. As these new approaches have been added, the sociology of science’ proportion of the total number of articles has declined (see Figure 2). On the other hand, a more reasonable comparison than pitting the sociology of science against the sum of all others would be to group it with scientometrics as representing the more traditional sociological approach and grouping the social studies of scientific knowledge, social studies of science and social epistemology together as roughly representing the “newer, more constructionist” approach (the sociology of knowledge is irrelevant here and was not included). When that is done the former dominates the latter in every year except 6 early years in which both sums were zero (total for all years are 745

24 versus 278). Moreover that predominance of the traditional sociological approaches includes the most recent 5 years (see Figure 3).

We tried to answer this question in three ways. First, one indication that they have not become distinct would be if their frequencies tend to rise and fall together and that is roughly what is observed (see Table 1.) Correlations of their frequencies are high and the spread among them is not great. The correlations are scattered across a range from a low of .33 between the sociology of knowledge and scientometrics to a high of .83 between scientometrics and the social studies of science. This tends to suggest that there are external forces acting on all of them which tend to increase or decrease their frequencies together. Much of that however is made up of the long term trend for all to increase.

The second approach is to consider conceptual overlap. To what extent are these descriptors in titles, keywords, or abstracts found in the same or in different papers? To determine conceptual overlap, the presence of competing expressions was searched for in sociology of science publications. Again, the evidence is unambiguous. Conceptual overlap between the sociology of science and others is minimal. The largest case of overlap is with the social studies of science - since 1975 14 publications – a meager 3% of all publications described as sociology of science

25 and 15 % of all publications described as social studies of science. Similarly, since 1971 the sociology of science has overlapped with the sociology of knowledge 13 times. This represents 2.7% of all sociology of science publications and an even smaller 1.9% of all publications described as sociology of knowledge. The overlaps for social epistemology, scientometrics and the sociology of scientific knowledge are even less – 1 and 0; 4 and 2; and 2 and 10 per cents respectively.

There has been a small increase in the total overlap of expressions recently – from 21/470 or 4% pre 2000 to14/143 or 10% for 2000 to 2007. In addition, in origin, there does appear to have been a close dependency of the social studies of science on the sociology of science. The first 4 articles (published from 1975 to 1980) described as social studies of science are also described as the sociology of science. It was not until 1981 that the first independent occurrence of the social studies of science is seen. In fact, the fragile state of the new expression initially is evident in 1982 when the sociology of science is mentioned in 5 publications in the journal Social Studies of Science, with no mention, outside of the journal title, of the social studies of science5. A similar situation is not observed with the others. 13% of articles in the sociology of knowledge, 16% of articles in the sociology of scientific knowledge, 46% in social epistemology, and 10% in scientometrics, were published independently, prior to their first co-occurrence with the sociology of science. Therefore, with the exception of the social studies of science in origin, these appear to have originated, and continue to exist, as separate species within science studies.

This fact is curious with respect to scientometrics in particular given the similarity of much of what is done under the two descriptors but the existence of such ‘sibling species’ speaks to the power of institutionalization in science as elsewhere in society. Scientometrics developed its own society (International Society for Scientometrics and Informetrics) and journal (Scientometrics) while the others tend to cooperate in 4S (Society for Social Studies of Science) and in journals such as Social Studies of Science, Social Epistemology, Episteme and Science, Technology and Human Values.

The third approach considers the extent to which sociology of science articles and traditional, more constructionist articles are published in each other’s journals. The obvious first step in this procedure is to define the parameters of the two types of journals. Our definition of sociology journals was quite strict. We included journals that contained ‘sociology’, ‘sociological’ or ‘sociology of’ in their title in whatever language. General social science journals were excluded as were a great many other kinds of journals. We made an exception for Social Forces which we know is a journal in which the authors are virtually exclusively sociologists. For more constructionist journals we included only journals focusing on the social aspects of science, the most important of which were listed above and excluded journals ‘of’ rather than ‘about’ science e.g. “Science” as well as the many journals in the history and/or philosophy of science. Our results for all the descriptors come from a total of 391 different journals. 15% of sociology of science and scientometrics articles (119/768) comes from sociology journals. Interestingly, 15% (43/281) of all constructionist articles come from their respective journals (see figure 4). According to our strict definition of what constitutes a sociology journal and a more constructionist journal, it is obvious that neither more constructionist nor sociology of science articles are disproportionately published in their own journals. Furthermore, as figure 4 shows, neither group publishes extensively across the field in each other’s journals.

11 Conclusion

In summary, quantitatively, it is clear that in science studies, judged by practitioners own designations of what they are doing, the sociology of science has not become extinct, it has not come to be dominated by other research expressions, and despite the addition of new descriptors, it has tended to maintain its distinctiveness. Obviously it is important to sociology that the study of a topic or field so important to the modern world as science not disappear, and that the sociological approach to it (e.g. Nakhaie 2007, Siler and McLaughlin 2008) not become overshadowed nor completely lose its distinctiveness.

NOTES

1 Twain’s quote was contained in a scribbled note available in scanned form at http://www.twainquotes.com/Death.html (access date: July 24, 2009).

2In this paper we ignore the important “technological” side of the subject. Hence we ignore descriptors such as “technology studies”, “the sociology of technology”, “the sociology of technical knowledge” and “social studies of technology” - “science in society” as it has been called as opposed to “society in science”. We also ignore the parallel terms which include both i.e. “science and technology studies”, “the sociology of science and technology”, “the sociology of scientific and technical knowledge” and “social studies of science and technology”.

3 Constructionism in turn is commonly said to have its roots in Berger and Luckmann‘s The Social Construction of Reality published in 1966. That claim with respect to origins is quite tenuous. In 1976, Bloor neither mentioned “constructionism” nor cited Berger and Luckmann’s book. Moreover, the disconnect makes sense in light of the fact that Berger and Luckmann’s book was primarily about offering a micro alternative to the macro sociologies of functionalism and conflict theory which were fighting it out in sociology at large at the time rather than emphasizing social construction in the subjectivist/relativist sense the term tended to take on, justified or not, in science studies.

4 Unfortunately several approaches that are to varying degrees prominent in science studies - particularly actor-network theory, ethnomethodology and feminist studies of science do not have descriptors commonly enough used which also differentiate their general use from their specific use in science studies or science and technology studies to enable us to employ them in our study. The one closest to having such a descriptor is “feminist science studies” but a preliminary looked showed this usage to be numerically insignificant. Undoubtedly however many studies in these genres are included in one or more of the three “constructionist” groupings that were

28 employed. An interesting footnote on the choice of descriptors is that recently adopted by the appropriate section of the American Sociological Association. In attempting to be inclusive, they adopted “Science, Knowledge and Technology” as their section title. However, in choosing such a unique expression they have undoubtedly frustrated the goal of making their research more visible. At the time of our searches there was exactly one item including this expression. In retrospect, it would have been much wiser to use what are in fact the most inclusive descriptors albeit not explicitly so i.e. “Science Studies” or “Science and Technology Studies”.

5 This finding appears to support Collins and Restivo’s use of the sociology of science and social studies of science as synonymous terms (1983). However, the fact that 85 per cent of social studies of science articles do not feature the sociology of science implies that they are different programmes.

Figure 1: Sociology of Science Counts

15 Publications…..

0 1957 1961 1965 1969 1973 1977 1981 1985 1989 1993 1997 2001 2005 Year Sociology of Science

Figure 2: Science Studies Publications - Proportionally

100%

90%

80%

70% P r 60% o p 50% o r t 40% i o 30% n

20%

10%

1949 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 Year S ocial S tudies of S cience S ocial E pistemology S cientometrics S ociology of S cientific Knowledge S ociology of S cience S ociology of Knowledge

Figure 3: Proportion of More Constructionist/Non-Constructionist Publications

100%

90%

80%

70% Proportion.... 60%

50%

40%

30%

20%

10%

0% 1957 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 Year

More constructionist non-constructionist

Table 1: Correlation of Publication Counts:

Knowledge

Scientometrics

SocialEpistemology

Sociology ofScience

Sociology ofScientific

Sociology ofKnowledge SocialStudies of Science

Social Studies of Science 1

Social Epistemology 0.60887 1

Scientometrics 0.833738 0.752238 1

Sociology of Scientific Knowledge 0.710999 0.604261 0.668562 1

Sociology of Knowledge 0.359496 0.363653 0.334022 0.391429 1

Sociology of Science 0.736292 0.619874 0.707022 0.680096 0.636424 1

Figure 4: Proportion of Articles Published in Sociology or More Constructionist Journals

16% 15% 15%

14%

12% 11%

10% Sociology Journals 8% More Constructionist Journals

6% 5%

4% % of articles published in articles of %

0% Sociology of Science, SSK, Social Epistemology, Scientometrics Social Studies of Science Descriptor

Chapter 4 The Evolution of Research Programmes: An Author Co-citation Analysis of Science Studies, 1949-2011

12 Introduction

In a recent paper Armstrong and Blute (2010) report on the ‘death of the sociology of science’ and investigate its supposed demise using bibliometric data. This paper moves beyond research questions that focus specifically on the sociology of science and instead analyzes trends that underlie changes in research programmes in terms of the content of their leading theorists. Formal research programmes form the basis of data collection. However the structure of the research field (science studies) is represented using author co-citation analysis and factor analysis. The research questions asked are: i) what is the level of conceptual variation in science studies?; ii) has this variation changed over time?; iii) if yes, what accounts for this change and its degree?

13 Sociology of Science and Theories of Scientific Change

There are many ways to conceptualize variation and change in studies of science. Collins and Restivo argue that contemporary science studies may be characterized according to research programme (strong programme, scientometrics, or laboratory studies) or in theoretical terms by a focus on Marxian (scientist as workers) or Weberian (scientist as professional elite) inclinations (1983). In his handbook of Science and Technology Studies David Hess (1997) argues that science studies have transitioned through three phases and currently resides in a fourth. He views the field as having gone from i) the philosophy of science, to ii) Merton’s institutional sociology of science, to iii) the strong programme of the sociology of scientific knowledge (SSK) and finally to iv) “where the field is moving” - broadly labelled as “critical and cultural

35 studies of science” - including “anthropology, critical social theory, cultural studies, feminist studies, critical technology studies, and the cultural history of science” (1997, p.3). Several authors mark the move away from the philosophy of science. This speaks to the more finely tuned distinctions within these phases that focus on the ‘context of discovery’ or the ‘context of justification’ as the main cleavage that characterizes early science studies (and whose seeming irrelevance now characterizes later science studies) (Leydesdorff, 1989; Dolby, 1971). Most prominently Kuhn’s (1962) introduction of revolutions in science identifies paradigms (and paradigm shifts) as being central analytic concepts while Merton’s (1937, 1942) functionalist sociology of science introduced the institutional norms of communalism, universalism, distinterestness, originality, and skepticism into science studies and explained science as a social institution employing central concepts like status as important variables. Interestingly, Collins and Restivo (1983) persuasively argue that despite their different employment by future authors, Kuhn and Merton’s theories both view science as stable, functioning institutions: “Kuhn is not only a Mertonian, but he is a Mertonian sans sociology” (p.190). Hess’ so-called third phase marks the dissolution of the theoretical boundary between ‘discovery’ and ‘justification’ and the movement towards sociological explanations of sociological knowledge itself (SSK), thus moving past the Mertonian-Kuhnian approach towards a more “constructionist” approach (Hess, 1997; Armstrong and Blute, 2010). Bloor (1976) is widely credited with the introduction of the strong program which then proliferated in the works of academics in Edinburgh and Bath (Mulkay, Collins, Barnes). In general terms the strong program can be epitomized by its commitment to reflexivity, causality, impartiality and, symmetry (Hess, 1997; Armstrong and Blute, 2010). Finally, Sismondo (2008) and Yearley (2005) have diversified Hess’ ‘where the field is heading’ to include such research programmes as actor-network theory and laboratory studies (Latour and Woolgar, 1979; Knorr-Centina, 1981), ethnomethodology and discourse analysis (Lynch, 1985), and studies of gender and science (Keller, 1985). In their own handbook of STS Jasanoff et al (2001) describes four models of scientific change that are not based on the grand works of preselected authors. Rather the models vary by their answers to six questions (social and cognitive dimensions) about scientific development: i) science as rational knowledge; ii) competition; iii) science as socio-cultural practice and; iv) extended translation. Most recently, Blute and Armstrong (2011) identify grand theories of science/scholarship offered by ten sociologists or sociologically-minded philosophers and use interviews and textual analysis to elaborate their similarities and differences on ten issues. The issues of greatest significance for

36 this paper include i) the nature and pattern of change, ii) the mechanism of change and, iii) the unique ideas.

Blute and Armstrong (2011) find that general theories of science/scholarship conceptualize change in four patterns: branching, linear, merging, and converging.2 Additional patterns include extinction or a cyclical pattern. Abbott, Hull, and Drori all see the nature of change as a process of branching. For Abbott, this occurs in a cyclical pattern he calls a ‘fractal cycle’ whereby competition between ideas results in winners and losers and the division that caused the initial split is recreated on a smaller scale among the winners. The final product largely resembles branching from a common ancestral lineage. Hull also argues that the nature of change in science is branching, but he argues that the pattern is evolutionary. Curiosity provides conceptual variation and additional ‘checking’ and ‘credit’ represent selection and descent accordingly. Drori’s ‘globalization of science’ argues that science is not uniformly practiced throughout the world and that its ‘styles’ are ‘glocalized’. For example, ‘cutting edge’ sciences emerge in nations that have the financial means to afford the requisite technology. So the ‘idea’ or culture of science spreads around the globe but its enactment is determined in large part by existing institutions. Ziman, and Frickel see science progressing in a linear fashion. For example, Ziman argues that Merton’s CUDOS were appropriate for academic science but have come to be replaced by the norms of PLACE3 in post-academic science. Frickel, meanwhile, proposes that scientific change proceeds in a fashion similar to other social movements and offers the notion of scientific/intellectual movements (SIMs) that encompass grievances, social structure, and cultural aspects. These social movements among scientists lead to the emergence of new research programmes that rise up and replace (or don’t) existing programmes. It is also possible that new programmes may coexist with existing ones by carving out a specialized area of activity or niche. Lastly, the philosopher Mario Bunge argues that science changes through emergence and by a pattern of convergence. Emergence, for Bunge, is the only source of novelty in science and this

2 The authors emphasize emergence because the previous lines of thought do not cease to exist. 3 “Knowledge may not be made public, work is done on local technical problems, governed by a managerial hierarchy, commissioned to solve specific problems, and the scientists is valued as a technical expert” (Blute and Armstrong, 2011, p. 404)

37 novelty emerges via “convergence” of previously unrelated lines of inquiry. The mechanism of change is ‘rational selection’ by individuals and the entire process resembles biological hybridization.

Broadly, Armstrong and Blute (2010) find that a Darwinian sociocultural evolutionary theory can successfully incorporate all of the useful unique ideas that characterize contemporary grand theories of scientific change. Novelty and repetition (cyclical) are handled by evolution’s descent with modification; all of the patterns of change that result in novelty are contained by evolutionary theory and selection is a universal mechanism that explains why different concepts, theories etc. spread or do not spread in science.4 Do these patterns and mechanisms of change accurately describe empirical data about science studies? This paper describes the evolution of the content of science studies’ leading theorists with an eye towards understanding how and why it has evolved.

14 Data Source and Methods

Data was collected from the Web of Science: a subsidiary of ISI Web of Knowledge. The database includes five citation indices: Science Citation Index Expanded (SCI-EXPANDED) -- 1899-present; Social Sciences Citation Index (SSCI) --1956-present; Arts & Humanities Citation Index (A&HCI) --1975-present; Conference Proceedings Citation Index- Science (CPCI-S) -- 1990-present; Conference Proceedings Citation Index- Social Science & Humanities (CPCI- SSH) --1990-present. Within the advanced search option, the most significant details are that TS is an operator for topic (i.e. key words or abstract) while TI is the operator for title. The use of quotation marks around the search terms returns only results that use that exact term, and the OR statement searches for results that satisfy either condition. The search string for the dataset was:

4 Theories of scientific change outside of sociology have been omitted for brevity (Bonaccorsi and Vargas, 2010; McCain, 1984; McCain, 1986; Moody, 2004; and Shwed and Bearman, 2010).

TS=("Actor Network Theory and Science" OR "Feminist Critiques of Science" OR "Feminist Science Studies" OR "Science and Technology Studies" OR "Scientometrics" OR "Social Epistemology" OR "Social Studies of Science" OR "Sociology of Knowledge" OR "Sociology of Science" OR "Sociology of Scientific Knowledge" OR "Strong Programme") OR TI=("Actor Network Theory and Science" OR "Feminist Critiques of Science" OR "Feminist Science Studies" OR "Science and Technology Studies" OR "Scientometrics" OR "Social Epistemology" OR "Social Studies of Science" OR "Sociology of Knowledge" OR "Sociology of Science" OR "Sociology of Scientific Knowledge" OR "Strong Programme").

The Web of Science’s ‘Create a Citation Report’ feature provides a detailed breakdown of how many times each publication in the search results was cited in every year. Publications were then sorted using Excel according to year and author. The author’s publications and citations (if more than one) were aggregated for each time period and the number of times each author was cited was then calculated. Those citation counts were then compared against the mean author citation rate for the time period and only those authors who were cited at a rate higher than the mean were selected to be paired with the highly-cited authors from the other research programmes.

15 Author Co-citation Analysis

Author co-citation analysis (hereafter ACA) is a citation method used in scientometrics to “investigate the nature of[structure and] changes in scholarly activity and associated changes in the intellectual, social, or cognitive structure of scientific specialties” (McCain, 1984, p.351). The main unit of analysis in ACA is the set of documents that co-cite the works of authors selected for analysis. In ACA an author’s work is considered in its entirety as an oevre (White & Griffith, 1981). Authors of interest are paired together and a frequency table is constructed that features a raw count of the number of times that any of author A’s work is cited in the same publication as any of author B’s (McCain, 1990; White, 1990a; White, 1990b). The frequency of papers that cite pairs of authors form the basis of a similarity or proximity matrix because as the

39 number of co-citations increases, it is argued that the author’s work is more similar (Leydesdorff, 2006) (See component 2, 1979-93 for an instance where competition can lead to similarity in co- citations). ACA provides the researcher with a data-driven view of the relationships between authors and the structure of the field more generally5.

16 Selection of Authors

A key component of ACA is how authors are selected to map the structure of a research area. Eom (2009) offers several insights ranging from purely objective to completely ad hoc. When understanding the evolution of research programmes in science studies, authors whose work was most cited in their respective programmes during the respective time periods were selected. This entailed amalgamating authors’ citation counts for all of their works during the specified time periods and determining the average citation count, per research programme, per period. Table 1 identifies the average citation counts for each research programme per period and the number of authors whose cumulative work in that period was in fact higher than the average6. This criterion was employed to recognize the divergent citation rates within different research programmes as well as their different historical evolutions. As Table 1.1 shows, the number of authors included in the co-citation analysis ranges in the three time periods studies from 31 in 1964-1978 to 60 authors in 1994-2011.7

5 See White &Griffifth (1981); McCain (White & McCain, 1998); and (McCain, 1984) for examples of ACA. 6 Please see table 1. Numbers in bold refer to the criteria used for author selection. Due to the increasing number of authors and publications and citations in later years the criteria for selection was modified to include authors whose work was cited at least as often as the average citation rate + 2 standard deviations from the mean. Blank cells refer to the fact that no authors published papers using that research programme as a keyword, in their abstract, or in a publication title during that time period. 7 Table 2 commences with the time period 1949-1963 however this period is omitted from the analysis because of too few authors to create a proximity matrix.

17 Proximity Matrix, Factor Analysis

This analysis employs the quantitative technique of factor analysis to map the evolution of science studies and uses the statistical package SPSS. However, before it can be accomplished a dataset of co-citation counts is constructed for every pair of authors selected for analysis in each time period. The program BibExcel was used to create a co-citation matrix to be imported into SPSS89. Once in SPSS a principle component analysis for each time period was performed. A principle component analysis (hereafter PCA) is a descriptive statistical tool whereby factors (or components) are derived based on the subset of authors who load on it (McCain, 1990). Furthermore, the PCA function also returns a component correlation matrix that identifies the relationships between the factors that are extracted from the data. The main ideas of each of the derived components for each time period can be found in Table 2. These main ideas are themselves derived from a qualitative analysis of the authors’ works who load heaviest on each component for each time period. The results are discussed below.

18 Findings: 1964-1978

A principle component analysis using the author co-citation counts from 1964 to 1978 results in the extraction of 4 components with eigenvalues above 1. 10 Cumulatively these components account for roughly 43 per cent of the variance in the data matrix (see table 2.1). The pattern matrix seen in Table 2.2 reveals how authors load on each component. The table has been

8 Following Leydesdorff (2009), the matrix from BibExcel was not standardized using Pearson’s R coeeficient because the data is already a proximity matrix. 9 For a detailed technical manual for using BibExcel please see: http://www8.umu.se/inforsk/Bibexcel/ollepersson60.pdf 10 The criterion of considering factors or components with an eigenvalue greater than 1 is known as the Kaiser criterion. This is the common standard used with principle component analysis for deciding which factors to include in the analysis.

41 simplified to only show scores for authors who load on each factor above ± .40. It is then the role of the researcher to analyze the individual loadings and to assign meaning to each factor based on the content of the authors who load heavily on it: generally above ± .70(highlighted).

In qualitative terms, the hidden theme that structures the first component is establishing the theoretical and methodological pillars that underpin the application of the sociology of knowledge to science. This specifically revolves around the significance of the logic of justification in the philosophy of science and the logic of discovery in the sociology of science. The first component for this time period explains roughly 43% of the variance and is comprised of 10 authors. Each author, ultimately, is involved in situating the sociology of science in its historical context and outlining (with different implications) the intimate connection of data and methods as justification/plan for its future direction. This occurs differently for the different authors. Cotgrove (1970), Dolby (1971), and Law (1974) weigh the heaviest on the first factor and can thus be analyzed to define its main characteristic. At the broadest level Dolby highlights the historical meandering between concerns about the logical validation of the outcomes of science (logical empiricism) and the more sociological concerns with science as a process of discovery (sociology of knowledge). Here he discusses the need to overcome Merton’s tacit acceptance of the empiricists’ rational and objective methods and to adopt a more Kuhnian approach that situates sociological factors within numerous stages of the very method of data collection and scientific methodology. Such an approach, Dolby argues, allows for a synthesis or ‘cross-fertilization’ of sociology of knowledge and philosophy of science that maintains its emphasis on the progression and objectivity of science while simultaneously accepting the role of theory and situating knowledge production in historical terms. In other words Dolby presents a justification for a new form of relativism in sociological studies of science that is consistent with the theoretical tenets of logical empiricism. This form of relativism is incorporated into Cotgrove and Law, who both seek to contextualize the scientific process in terms of the social processes involved in the creation and acceptance of norms and values by members of the scientific community. Cotgrove explicitly describes science as a social activity and situates scientists’ behaviour in a particular social context involving members of a community who seek recognition of creativity. Law similarly does not take norms for granted and argues that understanding cognitive consensus among scientists would benefit from including theoretical

42 tools from a social-psychological interpretative approach. This approach allows one to investigate the empirical building blocks of norms and paradigms and to make connections between methods, theories, and this data.

The specific contributions of the three authors presented above correlate highly with the first component due to their broad concern with theoretical matters Undoubtedly Robert Merton (1972) also correlates highly with this factor, though a closer analysis of his work from the period suggests that it correlates because of its more general sociological treatment of knowledge which the remaining authors both build on and ultimately transcend as they apply it more concretely to science as a particular field of knowledge production. Thus, the remaining authors who correlate highly with the first factor are similarly concerned with the emerging theoretical bases of sociological studies of science, but are also involved in testing these theoretical assumptions against empirical examples. Clarke (1968) and Mulkay (1974) explicitly begin their works by emphasizing the intimate connection between sociological factors, theoretical predispositions, and ultimately, substantive results.

The underlying structure of the second component [accounts for approximately 21% of the variance] is exclusively centered on the sociology of knowledge and, unlike the first component, focuses no attention on science as a specific form of knowledge. A brief look at the authors who load heavily on this component shows concerns with the problems of relativism for teaching (Young, 1973), an application of the sociology of knowledge to interpret terminology systems (Mckinley, 1971), and an attempt to integrate Gramscian concepts of ideology and hegemony within a Marxian sociology of knowledge (Salimini, 1974). Clearly the works of these authors represent developments within the sociology of knowledge that are both theoretically and substantively concerned with sociological issues other than science. Curtis (1970) and Fischer (1966) also weigh heavily on this component and their work correlates strongly with the theme of the application or refinement of the sociology of knowledge to community power research (Curtis) and trends in Soviet sociology (Fischer).

The common theme that the third component discovers in the data is one concerned with the theoretical and methodological consideration of micro and macro levels of analysis for sociological studies. Only two authors correlate uniquely and highly with the third component extracted from the co-citation data and it explains roughly 10% of the variance in the data. This component is exemplified by the works of Manis (1968) and Berger (1966). In his work on community health research Manis dissects the central concepts and theories using the sociology of knowledge to highlight extra-theoretical influences. For example, he argues that the absence of community-centric theories within the field has consequences for the validity and reliability of operationalization of the central topics including community and mental health. He also argues that the inductive approach employed by researchers in the field must recognize that sociologists are acted upon and influenced by specific groups and play an active role in the choice of theory and methodology. Berger (1966) continues with the theme of individual identity and accounting for the individual in the sociological endeavor with his critique that the sociology of knowledge has not (and should have) been integrated with the social psychology of George Herbert Mead. He argues that while the sociology of knowledge implicitly recognizes the role of subjectivity in the creation of knowledge, there is no explicit recognition of how individual reality is socially constructed. Social psychology is a useful tool for understanding how an individual is situated in a social milieu and what effect this dialectical relationship between self and social structure has for objective reality and internalized identities, or between objective and subjective aspect of reality.

Manis concerns himself primarily with problems of aggregation when defining sociological concepts such as community or mental health and the use of population characteristics (which are mere aggregates of individual level data rather than socio-cultural variables) in community health research. In a different way Berger is also concerned with micro level aspects of the sociology of knowledge. Where Berger differs from Manis however is in his desire to incorporate social psychological theory into the sociology of knowledge to provide a more nuanced theoretical proposition about the active role of individual cognition and social structure which interact to create objective and subjective reality which are internalized and reproduced. Both authors argue that there are theoretical and conceptual shortcomings of ignoring the dual

44 nature of consciousness (Berger) and sociological research (Manis) and research must consider multiple levels of reality to be valid and to give reliable accounts.

Lastly for this time period, the fourth component extracted from the data explains approximately 5 per cent of the data and is exemplified by Walton who is the only author that loads heavily on the component with no cross-loading across categories. Walton’s (1966) work provides the empirical basis of papers that followed temporally by Clarke (1968), and Curtis (1970). This work is noted for its reference to sociological factors as being ‘variables’ and is largely a descriptive account of the temporal ordering of research disciplines, research methods, and the outcome variable of interest (community power structure as conceived by researchers). Interestingly, two prominent authors (Ben-David, 1970 & Garfield 1971; 1978) load heavily on the first and fourth factor; a feature that helps to explain the slightly greater positive correlation (.443) between these factors1112. Garfield’s correlation with the first component demonstrates his concern with the history of science as a substantive area and his concern with theoretical and methodological challenges to his citation index. However, his strong correlation with the fourth component reflects his acceptance of methodological challenges to bibliometric work and his clear commitment to minimizing those challenges and enshrining citation data for the social sciences and science studies into his bibliometric datasets. Finally, Ben-David’s (1970; 1975; 1978) oeuvre for the period correlates strongly with both the first and fourth component in large part because of the historical focus of his work. Thus when Ben-David traces the pre-World War 2 developments within the Sociology of Knowledge he is actively identifying how theoretical and methodological variations resulted from concrete historical events. However, concurrent with this approach, Ben-David highlights the ways in which the social functions changed and thus resulted in changes to the structure of science. His analysis focused largely on the effects of

11 Phillips also correlate strongly with the first and fourth component however he had a very strong correlation with the third component which makes it difficult to categorize his relationship with the underlying themes in the dataset. 12 As discussed in the methodology section, rotation is a tool used to achieve a simpler structure of the data. Oblique rotation assumes that there is a possible relationship between the underlying components in the data. The component correlation matrix (table 3) shows that in fact all four of the components derived from the data are positively correlated with each other however the correlations are weak.

45 these changes on the rate of scientific production: a substantive area that is well-suited to analysis by means of bibliometric analysis as a representation of impact and future directions.

19 Findings: 1979-1993

In this time period there is an expansion in the overall size of the science studies community and the emergence of new, distinct research communities. With the increase in authors and articles since the previous period, it is no surprise that a principle component analysis extracts more components from the dataset. Compared with 4 components for 1964-78, the PCA extracts 7 components with the cumulative ability to explain 86 per cent of the variance. Of particular interest is the increased prevalence of the first latent factor (explaining 52% of the variance) with a more equitable distribution of variance being explained by the following three factors (7.9%, 7.4%, and 6.8% respectively). Compared with the previous time period, the first four components explain roughly the same cumulative variance (75% vs. 80%), however 3 additional components are extracted that have eigenvalues above the critical value of 1 (see table 3.1). An inspection of the component correlation matrix for this time period shows that all the extracted components are positively correlated with each other to varying degrees. The first and second components cumulatively explain 60% of the variance and are correlated strongly and positively with each other (.712). In fact, the first and second components tend to correlate fairly strongly with almost all of the extracted components. Of particular note though is the distinction between the second and third component in terms of their correlation with each other and of the third component’s correlation with other extracted factors. The second and third components explain roughly the same proportion of the variance (7.9% and 7.4%). However they are not strongly correlated with each other (.293), especially considering the strong correlation between the second and fourth and fifth components (.519 and .610). This demonstrates an emerging, independent component in the dataset that explains as much variance as other components that are more closely related to the predominant structure or theme in the data (component 1). Component 3 represents scientometric works that have moved beyond criticism and self- justification and operate independently of the established themes within science studies.

The first component can be thought of as a collection of authors who comment on developments within science studies away from Mertonian sociology of science yet offers distinctly sociological interpretations. This is most telling in a new found emphasis on the importance of conflict and competition in scientific practice. The first latent factor is characterized by the following authors who load heaviest on it: Holzner (1.307), Kuklick (1.215), Bates (1.167), Randall Collins (1.039), and Rollhansen (.983) (see table 3.2). Overall the first component explains 52 per cent of the variance within the data; a fact reflected by the broad scope of the authors who load positively and heavily on it. Holzner, for example, produced several articles within the time period which laid out the special relationship between the sociology of knowledge and the sociology of science. More specifically, Holzner (1982) analyzes, from a sociological perspective, knowledge structures and different forms of knowledge utilization as they are situated in practical enterprises. Secondly, Kuklick (1983) also focuses on the sociology of knowledge and evaluates the research programme’s movement back to Mannheimian roots and the greater incorporation of history and contextuality in sociology of knowledge research (including science). Perhaps most representative of the overall theme of the first component is Collins (1983) and Restivo (1987), who seeks to break down existing functionalist interpretations within the sociology of science and move towards a theoretical toolbox equipped with (especially) conflict, cooperation, and diversity. Other authors who load heavily on the first component also reinforce a distinct sociological aspect either through critique on a broader sociological basis (Delamont, 1987); a call to include sociological themes within the studies of science (Restivo, 1987); or the construction of knowledge claims within sociologically-analyzed settings (Tibbets, 1986). In all of these cases the authors demonstrate a willingness to move beyond the Mertonian and the Kuhnian sociology of science and apply a more critical eye towards the subject of analysis, be it in knowledge claims, theoretical issues of historicity, or incorporating conflict into any analysis of science.

The second component extracted from the PCA explains 7.98 per cent of the variance in the data and is strongly and positively correlated with the first factor (.712). Further inspection of the component reveals that the overall theme in fact builds on the new critical aspect of the first

47 component. The second component deals almost entirely with constructivism in science studies. The three authors who load heaviest on this factor include Leydesdorff (1.054), Radder (1.006), and Winner (.967) and they share a common theme: they are critical of the constructivist trends in science studies and they engage it directly. Conversely, many of the remaining authors who load heavily on the component can be viewed as defending constructivism (Mulkay, 1979; 1981; Smith, 1984; and Woolgar, 1988; 1991). Many of these authors also load above the .400 threshold on the first component and this clarifies the strong positive correlation between the first and second component. The second component is primarily a critique of the current state of science studies and authors who exemplify this approach (and characterize the first component) are drawn out as exemplars for the field. It is thus clear that even argumentation is a form of interaction.

The third component is perhaps the most telling indication of speciation within science studies. This third component represents the emergence of a unique research programme in science studies that appears to have gained independence from existing programmes and communicates very little with them. It explains roughly the same variance in the data as the second component (%7.49 and %7.98 respectively) yet for such a large component it correlates weakly with the other three prominent components (see table 2.3). The weak correlation between the 3rd component with the 1st, 2nd, and 4th is substantiated when looking at individual authors. Compared to the first and second component, significantly fewer authors load heavily (above .400) on the third component. This means that there is little interaction or communication between these authors and others. Analysis of the three primary authors who load heavily on this component clearly shows a new species: quantitative, scientometric analyses within science studies. To be clear, discussion of scientometrics also figured prominently in the second component, however, there it was still discussed in relation to the existing/dominant approaches within science studies. The authors who load heavily on the third component have moved beyond critique and make little to mention of sociological or constructivist approaches in their work. The radioanalytic chemist, Lyon (1984, 1984b, 1985), loads heaviest on the third component (1.058) because his work is both a reflexive account of a scientist about science studies - his report from the annual meeting of the society for social studies of science (4S) (1984) declares that he and his fellow scientists and engineers are the subject of sociological

48 analysis - and because he moves past critiques of sociology and presents a quantitative, scientometric analysis of communication at scientific meetings. Lyon’s work loads negatively and weakly with every other component derived from the data: he truly represents a novel departure in science studies. Similarly, Lenoir (1979; 1979a) argues that co-citation analysis is better suited than historical qualitative work at identifying the core literature within research programmes and exploring the link between cognitive development and social development: a characteristic of Mertonian Sociology of Science that has been lacking. Lastly, Moravcsik (1985) and Lindsey (1980) both offer technical appraisals of scientometrics, though for different causes. Lindsey approaches methodological problems in quantitative ‘counting’ methods of scientometrics on their own ground and offer a resolution that remains firmly within the quantitative sphere. Moravcsik goes further and explains how scientometric analysis can provide indicators to measure scientific development in developing countries with an eye towards measuring progress, activity, and productivity. The theme of this third component and its relatively high share of descriptive power point clearly to a research programme that has evolved within science studies and relies little on the substantive content of other programmes (or components).

The fourth component extracted for the time period that explains more than 5 per cent of the variance in the data is concerned with defending the traditional sociology of knowledge, or rather, evaluating the efficacy of sociology of science theorizing for comprehending the cognitive aspects of scientific development. This fourth component explains 6.85 per cent of the variation in the data and only Harvey (.719) and Weiss (1.163) load heavily on it. Weiss (1987), for example, analyzes social-psychological theories of values, attitudes, and beliefs within organizational theory to determine whether their treatment of ideology serves as a suitable basis for managerial decision making surrounding the treatment of alcoholism. He concludes that in fact the over-reliance on ideology as a social-psychological term in organizational theory has not been fruitful and that there is a need to incorporate social structure to expand understanding. Harvey (1982;1987) defends traditional sociology of knowledge as well but unlike Weiss, his targets are sociologists who have glossed over the theoretical intricacies of Kuhn’s notion of paradigm and applied it, mistakenly, in contemporary sociological studies of science and knowledge development.

Prominent names in science studies load heavily on the 5th component. Bloor (.459), Cozzens (.478) and Delamont (.530) all load heavily on the component however David J. Hufford (1.197), a Professor of (among other things) Folklore, weighs the heaviest. The fifth component explains 4.66 per cent of the variance in the dataset. The underlying theme of this component is, broadly, a shared focus among the leading authors on the cognitive aspects of the sociology of science and whether they are lacking in the current framework. Cozzens (1985) for example presents an analysis of how citations to two specific scientific papers changed over time, and how it extends beyond social structure. David Bloor’s (1982) discussion of Durkheim, Mauss, and the impact of classification for systems of knowledge is highly relevant as he situates knowledge not only in terms of being socially influenced, but being an influence as well. Finally Delamont (1987) suggests learning environments and the socialization of scientists as one of the “blind spots” in the sociology of science. Therefore, broadly, this component and its leading authors are largely concerned with matters that transcend social structural concerns in the sociology of science, and each looks to the cognitive practices of individuals for answers.

The use of Kuhnian paradigms is the underlying theme that best defines the sixth component (3.48 per cent of the variance); however it is not as consciously employed by Domhoff (1.062) as it is by Harvey (.649). Harvey (1982) explicitly challenges contemporary applications of Kuhn’s paradigms in the sociology of knowledge. Conversely, though not explicitly, Domhoff’s (1987) treatment of theoretical developments in the interpretation of the Social Security Act may be interpreted as an attempt at a Kuhnian sociology of knowledge. Domhoff repeatedly uses language as a metaphor to illustrate the disjuncture between various Marxian factions in their treatment of important political acts.

The final component is represented by two authors whose focus is scientometric in both methods and substance, and represent 3.16 per cent of the variance in the data. Yablonsky (.886) and the founding father of the ISI, Eugene Garfield (.578), are the only two authors who load heavily on the final component. Yablonsky’s (1985) contribution to Scientometrics is an extremely technical analysis of the Zipf-Pareto law commonly employed in scientometric analysis. Scientometrics itself is the topic of study. Garfield’s prominent contributions during this period

(1979; 1992) also characterize this component as they: 1) chart the history of scientometric analysis and its institutionalization in the journal Scientometrics and; 2) an analysis of the productivity and impact of Nobel laureates using citation counts. Interestingly, the final component is correlated positively with the others from the period, however, it has a noticeably weak correlation with the third component which is decidedly scientometric as well (see Table 2.3).

20 Findings: 1994-2011

As with the first two time periods, the first component extracted in the PCA explained a large percentage of the variation in the data (51 percent). Furthermore, 27 authors load on the first component above the cut-off of .400. It is not within the scope of this paper to comment on the contribution of all 27 authors; however the contributing research programmes can be identified before analyzing the heaviest loading authors (see Table 4.1 and Table 4.2 below).

There is great diversity in the research programmes that load heavily on the first component with 9 of the 11 programmes represented. However, the underlying theme that unites these authors can be drawn from an analysis of the authors who load heaviest (see Table 4.3). In the case of the first component the oeuvres of Berg (1.011), Marcus (.946), and Gherardi (.948) offer a glimpse into the thematic aspects that bind these authors together and help explain much of the variation in the data from 1994 to 2011.

The first component is built centrally, be it methodological or theoretical, around themes from Actor Network Theory (ANT). For example, Marcus (1995) concentrates on the new movement within science studies towards multi-cited ethnographic fieldwork. Gherardi (1999) and Berg (1996; 2000) concentrate specifically on the notion of artefacts as they have been theorized in ANT and apply it to theoretical concerns such as Organizational Learning Theory and the

51 sociology of knowledge or a new understanding of medical records and their role in re- evaluating ‘representation’. These themes of representation, ethnography, and artefacts do not only occur in the works of the top three authors. In fact, the next 6 authors who load heaviest on this component all share a common concern with these themes. Whatmore (2000;2006) and Murdoch (1997) speak at length of the ability of ANT to resolve dualisms in science and technology studies between humans and non-humans and ascribe a creative role to such socio- technologies. Other authors similarly discuss various aspects of ANT from a concern to applying the results of STS research findings (Roth, 1996; 1997; 1998a;1998b, Demeritt, 1996; 2001), to issues of ‘localization’ and politics as products of resolving dualisms that typified previous theoretical approaches (Shapin, 1995; Slocum, 2004). What is clear is that the first component derived from the data set represents a new trend in science studies; a trend institutionalized largely in social geography and theorized in ANT. More than anything it demonstrates a concern with new methodological questions and the theoretical implications of multi-site ethnography. Lastly, this component focuses on applying the findings from STS research to either inform or generate political events.

There is an unmistakable theme that unites the authors of the second component - Social Epistemology. More specifically, and importantly, these authors concentrate on epistemic issues surrounding library sciences, information sciences, and education. The second component extracted from the PCA explains 9.86% of the variation in the data and compared to the first component features a sharp reduction in the number of authors who load heavily on it (4). Don Fallis, Alvin Goldman, and Thomas Uebel are the preeminent authors that load heavily on component 2 (.903, .713, and .788 respectively). Fallis (2001, 2004) argues that social epistemology is relevant for the information sciences because the decision of what materials to include and exclude in a collection necessarily involve epistemic values and are necessarily social because they are the material that forms a social connection between the provider and recipient of knowledge. Goldman (2006) similarly applies social epistemological theory to evaluate whether intelligent design should be taught in biology classes. Thus, social epistemology is concerned here with evaluating educational practices in terms of knowledge transmission and the identification of what constitutes experts and how they are to be involved in curriculi (knowledge transmission) decisions. Lastly, Uebel (2000) is also concerned with

52 epistemic issues, though more so with the divide between logical empiricism and the sociology of knowledge. Uebel argues that there is in fact the possibility of a middle ground between the process of validation of scientific results (the domain of logical empiricists) and the context of discovery (sociology of scientific knowledge). Component two is clearly representative of [social] epistemological applications to questions of value theory and the sociological aspects of knowledge transmission. Whereas these themes may also be generally covered by authors in the first component, the specificity of the term social epistemology as well as only a passing reference to science specifically serve as points of demarcation between component 1 and component 2.

Component 3 is similar in certain respects to the second: it explains much less variation (5.86%) than the first, and there are only 5 authors who load on it above +.400. Of these five authors three load heavier than the rest and their work does well to help to identify the theme that unites them and separates them from other authors. James Moody (2004) loads the heaviest on the third component (.934) and his work deals with a sociological analysis of collaboration networks and their effects on the scientific practice and the structure of ideas. This work loads heavily on the third component in part because of its breadth: it employs counts of authors from sociological abstracts to determine collaboration networks (quantitative, bibliometric techniques); its explanandum is the content and structure of sociology and sociological ideas (SSK) and; its explanans involves social interaction, typified through social networks. Vinkler (1996; 2000; 2004; 2007) loads heavily on the component (.843) and is also interested in eminence in science. However, his concern is primarily around the technical aspects of scientometric indicators such as the Garfield Factor and the h-index. In this respect he shares with Moody a concern for questions of impact in science (Moody’s ‘area-authorities’) and he is similarly involved in advanced quantitative methodology that maps the structure of research programme and intellectual structures. Lastly, Leydesdorff (1997; 1998; 1999; 2007) loads heavily on the third component (.833) in no small part because of his focus on tying bibliometric methodology together with the theoretical aspects of social network analysis. Leydesdorff endeavours to map communication networks using bibliometric indicators (co-citation, co-author, etc) to expand the theoretical components of scientometrics.

If the third component of the PCA was involved in a sociologically-minded scientometric analysis, then the fourth component is properly understood as scientometrics without the explicit attention to sociological themes such as networks. The fourth component explains 4.44 per cent of the variance in the data and contains 4 authors who load above .400. Of the four, the heaviest loading belongs to the creator of the Science Citation Index and HistCite Software: Eugene Garfield (.965). Garfield’s (1995; 1998; 2004; 2007; 2010) contributions can be conceived as being largely institutional, introducing new technologies and methods in scientometrics. For example, Garfield used his own HistCite software to situate himself in the evolution of scientometrics; the publication activity of knowledge domain literatures; and reviews the pragmatic role of scientometrics as a tool for research evaluation. Ying Ding (loading: .842) similarly engages in bibliometric analysis and her work also engages in both methodology and the application of said methodology. Ding’s (2000; 2001) speciality involves co-word analysis and focuses on the field of information retrieval and the creation of more reliable datasets for end-users of bibliometric analysis. Lastly, Martin Meyer’s (2000; 2004; 2010) research on extending scientific citation analysis to studies of patent citations and his research on the development of the emerging field of Computational and Mathematical Organization Theory ties his oevre together with the empirical, scientometric theme that exemplifies the fourth component. His loading of .703 is high and marks his role at the forefront of bibliometric research.

The fifth component derived from the PCA explains 4.35 per cent of the variation and, like the second, third, and fourth component, has a small number of authors who load on it over the value of .400. Bruno Latour (.965), Neil Coulter (.737), and Loet Leydesdorff (.592) are the only authors who load heavily on the extracted component and this is somewhat complicated by Leydesdorff’s high loading on factor 3. What is particularly strange about this component is the high loading of Bruno Latour and his low loading on the first component that was previously described as being entirely ANT-related. However a closer look at the authors’ work in this period shows that the fifth component is representative of a move towards greater interdisciplinarity. For example, Latour (2000; 2002; 2003) variously argues that the current state of sociology would benefit from modifications from STS. He goes on to argue that the late theorists Gabriel Tarde is an exciting theorist for contemporary sociology given the discipline’s

54 shift towards psychologism, its importance for the sociology of science, and the breakdown of the nature/culture divide. In other words, Latour has moved past the theoretical pronouncements that underpin the work in component 1 but he takes those changes as given and is moving sociology to a new direction. Coulter (1998) and Leydesdorff (1997; 1998; 2007) implicitly build on this theme in their pursuit of interdisciplinarity. Coulter applies scientometric analysis (co- word) to the field of software engineering to understand emerging and regressing trends. Leydesdorff also focuses on scientometrics and attempts to bring it in line with sociological theories of social networks and cultural evolution. He even explicitly seeks a theory-driven type of scientometrics. This component is best described as one seeking interaction between different research programmes with the aim of increasing the ability of both programmes to describe social reality. This is a form of emergence by merger.

The sixth, seventh, and eighth components cumulatively explain 8.96 per cent of the variation in the data (3.62, 2.71, and 2.63 per cent respectively) and cumulatively only have 5 authors who load above .400 on them. Furthermore, components 6, 7, and 8 have very low correlations with each other and with the remaining components. Black (.974) and Irwin (.482) weigh on the sixth component and both figure in discussions on the history of information. Black (2006) relates this information through a detailed historical analysis of the component parts of information history, while Irwin’s (2006) discussion of new scientific governance and genetically modified food involves the ways that information can be utilized to redefine public talk. Clemens (.896) and Strauss (.928) are the only two authors who load heavily on the 7th component and their work can be collectively conceived as a sociological analysis of scholarly reputations as indicated by publication histories. Clemens (1995) offers an empirical overview of differences between gender, rank, and genre and how they relate to publication rates. Strauss (2008) no doubt contributes to this theme through his own autobiographical account of his career in family violence research. Here he offers observations that tie personal characteristics as well as interactional characteristics to his own scientific career. Finally, Nettleton (.981) is the only author who loads above .400 on the 8th and final component. Her work in social epistemology is strikingly similar to the work typical of the second component. She is interested in expert knowledge and whether social policies relating to the internet and e-scaped medicine can transcend structural inequality. No doubt the applied aspect of Nettleton’s (2003) work is what

55 distinguishes it from the work of the second component. Whereas those scholars were interested in theoretical and epistemological questions pertaining to social epistemology and information science, Nettleton’s analysis is decidedly more sociological in its inclusion of structural inequality and real social policies.

21 Discussion

21.1 What is the level of substantive variation in science studies? 21.2 How has this variation changed in time?

The number of components derived in the PCA and the variation they each explain provide important clues about the nature of variation that exists in any given time period. Recall that in table 2.1 four components are derived from the data for 1964-1978. Together they explain 80 per cent of the data. Interestingly, each of the second through fourth components explain half as much variation as the previous one, despite relatively smaller declines in the number of authors per component. For example, the first component explains 43 per cent of the variance and has ten authors who load above .40 on it. The second component explains 21 per cent of the variance and has 7 authors who load above .40. This, along with the weak correlations between the individual components (see Table 2.3) suggests four relatively distinct substantive areas within science studies with little intercommunication between them. This time period is also characterized by a relatively dense environment. Across the four components 27 authors load above .40 and explain a cumulative 80 per cent of the variance; this returns a density of 2.96.

By the time period 1979-1993 there appears to be an increase in communication with other communities (see table 2.3). The overall density in the period is down to 2.04 as more components are derived from the data (7) as well more authors (42) that load above .40 and a slightly larger explained variance (86%). The change in value of the variance the second

56 component explains is particularly interesting in this time period. Whereas in the first time period the second component explained roughly 20% of the variance and was weakly correlated with the first component, from 1979-1993 the second component explains much less variance (7.9%) and is quite highly correlated with the first, prominent component with a correlation of .712 (see Table 3.3). Overall it appears that communication between the components (substantive areas in science studies) has increased compared to the previous time period as many of the components are positively and moderately correlated. This means that there is a less discernible difference between the substantive bases of the components – merging. Rather what may be occurring is in fact ‘minor tweaking’ of main ideas.

Lastly, from 1994-2011 the density again drops to 1.75 as 48 authors who load above .40 are distributed over 8 components that explain 84 per cent of the variance. Consistent with the previous time periods the first component explains much more variance than the rest (51 per cent in this case). Furthermore, the component correlation matrix shows that other than a moderate and positive correlation between the first and second component, no other components are even moderately correlated (Table 4.4). The third through eighth components also explain less variance than previous years and contain fewer authors who load above .40. So there is some degree of similarity or communication between the practitioners of the first and second component but by and large there is a degree of homogeneity within the data that was previously missing.

21.3 What accounts for this change?

In simple descriptive terms, it could be argued that since 1964, more practitioners have written in science studies but they are tending to do so more and more in the dominant substantive or methodological paradigm. This would explain why, despite reduced density in each period, the first and second components tend to be positively and strongly correlated with each other (and in the middle years with other components). What was happening in each period that could explain

57 this? In the first time period the dominant component (that explains the most variance) is clearly establishing the epistemological and ontological bases of the sociology of science, especially as it relates to logical positivism and the philosophy of science generally. The second component, unrelated according to the component correlation matrix, explains roughly 20 per cent of the variance and is concerned primarily with matters pertaining to the sociology of knowledge with no concern for science. So between the first and second component there is hardly a fine-grained difference and instead the sociology of science, in general terms, carves out a niche for itself in science studies that includes independence from the sociology of knowledge. The second time period is a period of refinement as there is an influx of practitioners to the field but the field itself has expanded from 4 to 7 components, thus reducing the overall density. In this period the primary component (#1) explains more variance than it has in the past and it is strongly related to 3 other components that all correlate with it above .500. This is both a period of refinement and of branching. The first component exemplifies the refinement of the sociology of science and the firm identification of its roots. Highly related to this is the second component that, presumably, accepts many of these foundations yet offers modifications which are substantively ones that will usher in sociological accounts of scientific knowledge, or SSK. Thus the strong communication between these factors does not necessarily signify agreement or cooperation, rather competition and some conflict between substantive areas within science studies that were previously lacking. This conflict is evident in the third factor, substantively identified as quantitative, scientometric analysis, as it explains about as much variance as the second factor yet appears to communicate very little with the other substantive areas. This represents speciation within science studies. The final time period corresponds nicely with the assertions made by Hess (1997), Yearley (2005), and Sismondo (2008) as there is variation. However, it has occurred within the primary component. There is a substantial increase in the number of authors who load on the first component (27) and they represent themes that encompass actor network theory, discourse analysis, as well as ethnomethodology. Since this component explains 52 percent of the variance in the data, this is truly the ‘where it’s heading’ of science studies. The lower overall density of the time period (1.75) and the almost complete lack of interaction between the remaining 7 components suggests that the ideas espoused in the first component have eclipsed, and are not dependent, on the ideas of the other components (or their historical relatives). In fact, as one progresses down the remaining components there is a clear super-specialization that is occurring, though this may be largely due to the inclusive nature of the first component. One might argue

58 that the fields occupied by the first component have a high carrying capacity. This means that it contains much of the diversity that exists in the area and it has a high density.

22 Conclusion

As previously discussed, theorists have attempted to classify the evolution of science studies in terms of research programmes. However this analysis has shown that in any given time period several different approaches exist and the lines between them are anything but rigid. Research programmes do not merely replace one another. Instead science studies evolve in a combination of branching, convergence towards similar characteristics, linear development, and merging. There is constant communication between scholars and research programmes though the nature of this communication is not always clear. This finding is largely a consequence of the method used to derive it. ACA (in this study) relies solely on journal articles. It is obvious that this presents an incomplete look at the entire intellectual structure of science studies. Journal articles may be more concerned with building upon the theoretical foundations that have been established in books by prominent authors. Even the post-hoc sorting of material into schools or programmes is likely to occur in handbooks and anthologies. This paper reports on the ‘mass’ of output in science studies. We can expect that the distribution of authors who align on any issue, substantive or methodological, will do so in a way that resembles a normal distribution. At the two tails, or extremes, we should expect to find authors who either defend the position, or those who attack it heavily and call for change. These are the authors I suspect would be represented in books. However, the ‘mass’ of authors are those who mediate between the outliers. They are the authors who apply the works of the ‘giants’ in the field to their own particular case. These are authors who are less concerned with remaining at the forefront of the programme and instead seek to fill new found space for publishing, attending conferences, and taking on new students. The data presented in this paper are largely that middle ground. It shows communication in many forms: competition, conflict, cooperation. It is balancing extremes to produce the greatest conceptual variety with an eye to conceptual fitness. So, this analysis cannot argue that research programmes are invalid for explaining the content of science studies. The evolution of ideas in

59 science studies in messy and any analysis that relies solely on a single representation of academic output will miss telling the full story. Table 1.1: Selection of Authors to Include in Factor Analysis: Mean citation count of authors by research programme and time period.

1949-63 1964-1978 1979-1993 1994-2011 mean authors mean authors mean av+1sd authors mean sd av+2sd authors Actor Network Theory and Science 1.0 2.4 1 14.5 47.7 110.0 5 Feminist Critiques of Science 2.5 3.2 1 8.8 14.9 38.5 1 Feminist Science Studies 4.3 7.9 20.2 2 Science and Technology Studies 0.6 1.8 3 9.1 28.9 66.8 8 Scientometrics 1.8 1 5.1 20.0 5 7.2 19.4 46.0 9 Social Epistemology 0.0 0.3 1.0 4 3.5 5.9 15.2 7 Social Studies of Science 0.2 1 14.0 66.4 4 15.0 25.1 65.1 5 Sociology of Knowledge 0.1 2 1.5 23 0.8 3.8 13 6.3 17.7 41.7 10 Sociology of Science 2.2 1 1.2 6 2.4 9.0 10 8.6 28.1 64.9 7 Sociology of Scientific Knowledge 3.2 11.0 3 10.2 18.0 46.3 5 Strong Programme 1 1.6 2.3 6.2 1 Total: 3 31 45 60

Table 2: The Main Ideas for Each Component by Time Period

Component 1 2 3 4 5 6 7 8 Sociology of micro/macro Logical variables' and knowledge, no considerations 1964-1978Positivism, Logic the logic of attention to in science of Discovery inquiry science studies constructivism: Methodology cognitive conflict, conflict and the place of and speciation: aspects of Kuhnian Time 1979-1993cooperation, competition cognition in institutionalizati scientometrics scientific paradigms Period diversity with existing theory on of development paradigm scientometrics Social Social networks: scholarly social ANY: epistemology: centered on collaboration, reputations: epistemology: at 1994-2011ethnography, more scientometrics Emergence issues of communication, measurement a very applied artefacts epistemological 'information' eminence and effects level at its core

Table 2.1: Total Variance of Author Co-Citation Counts by Extracted Factors: 1964-1978*

Initial Eigenvalues Extraction Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % 9.047 43.081 43.081 9.047 43.081 43.081 4.441 21.148 64.229 4.441 21.148 64.229 2.254 10.736 74.964 2.254 10.736 74.964 1.117 5.320 80.284 1.117 5.320 80.284 .848 4.040 84.325 *Extraction Method: Principal Component Analysis.

Table 2.2: Correlation between Authors and Components: 1964-1978* Component

1 2 3 4 BendavidJ .446 .478 BergerP .566 ClarkT .822 ClinardM .694 CotgroveS .930 CurtisJ .758 DolbyR .867 FischerG .694 GarfieldE .531 .642 HorowitzI .408 .484 LawJ .965 ManisJ .757 MckinleyR 1.024 MertonR .771 -.640 MulkayM .657 PhillipsD .648 .649 -.447 PriceD .754 SalaminiL 1.024 WaltonJ .787 WarrenR .598 .437 YoungM .884 *Extraction Method: Principal Component Analysis. Rotation Method: Promax with Kaiser Normalization. a. Rotation converged in 7 iterations.

Table 2.3: The Correlation between Components: 1964-1978* Component 1 2 3 4 5 6 7

1 1.000 .712 .364 .565 .606 .345 .432 2 .712 1.000 .293 .519 .610 .171 .401 3 .364 .293 1.000 .261 .346 .221 .319 4 .565 .519 .261 1.000 .522 .372 .298 5 .606 .610 .346 .522 1.000 .327 .402 6 .345 .171 .221 .372 .327 1.000 .252 7 .432 .401 .319 .298 .402 .252 1.000 *Extraction Method: Principal Component Analysis.

Rotation Method: Promax with Kaiser Normalization.

Table 4.1: Research Programmes and Authors Included in Component 1: 1994-2011 Calas, Callon, Goodman, Murdoch, Actor Network Theory and Science Whatmore Feminist Science Studies Berg Science and Technology Studies Brown, Irwin, Marcus, Sheller, Whatmore Scientometrics Martin, Vinkler Social Epistemology Bassett, Fuller, Garrison Social Studies of Science Demeritt, Jasanoff, Murdoch, Roth Sociology of Knowledge Gherardi, Olick, Scott, Shapin, Swidler Sociology of Science Dasgupta, Callon, Long, Lynch Sociology of Scientific Knowledge Barnes, Demeritt, Lynch, Roth, Shapin

Table 4.2: Total Variance of Author Co-Citation Counts by Extracted Factors: 1994-2011*

Rotation Sums of Squared Initial Eigenvalues Extraction Sums of Squared Loadings Loadingsa Component Total % of Variance Cumulative % Total % of Variance Cumulative % Total 1 23.097 51.327 51.327 23.097 51.327 51.327 22.688 2 4.439 9.865 61.192 4.439 9.865 61.192 10.542 3 2.646 5.879 67.072 2.646 5.879 67.072 5.155 4 2.000 4.444 71.516 2.000 4.444 71.516 5.251 5 1.960 4.355 75.871 1.960 4.355 75.871 3.578 6 1.630 3.622 79.493 1.630 3.622 79.493 2.302 7 1.220 2.710 82.203 1.220 2.710 82.203 2.126 8 1.185 2.634 84.837 1.185 2.634 84.837 2.044

*Extraction Method: Principal Component Analysis.

Table 4.3: Correlation between Authors and Components: 1994-2011*

Component

1 2 3 4 5 6 7 8 BarnesT .856 BassettK .690 BergM 1.011 BlackA .974 BrownN .888 CalasM .754 CallonM .898 ClemensE .896

CoulterN .737 DasguptaP .756 DemerittD .915 DingY .842 FallisD .903 FullerS .700 GarfieldE .965 GargK .644 .445 GarrisonJ .794 -.422 GherardiS .948 GoldmanA .713 GoodmanD .908 IrwinA .749 .482 JasanoffS .839 LatourB .765 LeydesdorffL .833 .592 LongJ .600 .420 LynchM .842 MarcusG .946 MartinB .495 MclaughlinN .469 MeyerM .703 MoodyJ .934 MurdochJ .938 NettletonS .981 OlickJ .586 RothW .936 ScottA .891 SecordJ .470 ShapinS .926 ShellerM .898 SlocumR .903 StrausM .928 SwidlerA .878 UebelT .788 VinklerP .843 WhatmoreS .939

Extraction Method: Principal Component Analysis. Rotation Method: Promax with Kaiser Normalization. a. Rotation converged in 13 iterations.

Table 4.4: The Correlation between Components: 1994-2011* Component 1 2 3 4 5 6 7 8 1 1.000 .556 .260 .249 .137 .119 .110 .159 2 .556 1.000 .183 .090 -.055 .070 .037 .312 3 .260 .183 1.000 .407 -.072 -.037 .167 -.015 4 .249 .090 .407 1.000 .288 -.047 .024 -.003 5 .137 -.055 -.072 .288 1.000 -.019 -.075 -.061 6 .119 .070 -.037 -.047 -.019 1.000 -.140 .090 7 .110 .037 .167 .024 -.075 -.140 1.000 -.091 8 .159 .312 -.015 -.003 -.061 .090 -.091 1.000 *Extraction Method: Principal Component Analysis. Rotation Method: Promax with Kaiser Normalization.

Chapter 5 Species of Science Studies

In his Science as a Process (1988) David Hull outlines the detailed history of the conflicts within systematics in the 1970s and 80s between three competing schools of thought: numerical taxonomy, evolutionary taxonomy, and cladistics. Each approach to ordering has distinct characteristics and each has a different capability of dealing with homology and homoplasy. 13 These are traits that are derived from a common ancestor (homologous traits) and those that result from convergence (analogous traits) (Blute, 2010). My research Species of Science Studies implicitly uses two of the three schools of systematics and the third is an opportunity for future research.

23 Numerical Taxonomy

Numerical taxonomy is a system of classification based on overall similarities and differences between groups of organisms (Sokal & Sneath, 1963; Mayr & Bock, 2002). Reports of the Death of the Sociology of Science Have Been Greatly Exaggerated (Armstrong & Blute, 2010) is a numerical taxonomy of science studies. The sociology of science and other research programmes are classified according to their overall fit using search criteria. There are necessary and sufficient conditions for an article to be included as a member of a research programme (taxon). I search for the name of research programmes in article keywords, titles, and abstracts, and the resulting counts are groupings of how similar or different each article is with respects to those criteria. A consequence of this taxonomy is that it is difficult to ascertain the evolutionary relationship of the programmes because the taxonomic characteristics are decided in advance and without in-depth analysis it is difficult to determine whether articles with shared characteristics have done so because of common descent or convergence.

13 I follow Mayr & Bock (2002) and use the term ordering and not classification. Classification may be involved in ordering nature but not necessarily.

24 Evolutionary Taxonomy

The Evolution of Research Programmes: An Author Co-citation Analysis of Science Studies, 1949-2011 relies implicitly on Ernst Mayr’s “biological species”. I derive the components for each time period according to level of similarity and difference in co-citations – social interaction analogous to genetic recombination. This is akin to interbreeding among authors. This analysis produces a historical grouping of species (authors clustered into research programmes) and the introduction of new species from groups of species. The varying social interaction between research programmes yield new components in future time periods that differ from those that they follow. Scientometrics is a strong case because we see it emerge in interaction with the sociology of science and as it becomes more and more technical it ceases communication with its ancestor.

25 Cladistic Analysis

Cladistic analysis has historical roots with the entomologist Willi Hennig who proposes that the ordering of species must overcome the superficial similarities of numerical taxonomy. He advocates a classification system based on historical similarities and differences (species ordered based on a common descent). The result is a cladogram comprised of clades: “parts of a phylogenetic tree” (Mayr & Bock, 2002, p.183). New taxa emerge from the process of “budding” as new lineages separate from the parental ones. New branches now exist independently from the parental line. Cladistic systematics is an area of future research for species of science studies. I can specify morphological characteristics to organize citation data and use cladistic software to produce cladograms. What should result, in combination with my current findings, is a taxonomy of science studies that traces the historical evolution of the field within its broader context (ecology).

References

Abbott, Andrew. Chaos of Disciplines. Chicago: University of Chicago Press, 2001.

Armstrong, Paul, and Marion Blute. "Reports of the Death of the Sociology of Science Have Been Greatly Exaggerated." Canadian Review of Sociology 47 (2010): 431-44.

Barnes, Barry. "Social Life as Bootstrapped Induction." Sociology 17 (1983): 524-45.

Basalla, George. The Evolution of Technology. Cambridge: Cambridge University Press, 1999.

Bates, R. J. "Educational-Administration, the Sociology of Science, and the Management of Knowledge." Educational Administration Quarterly 16, no. 2 (1980): 1-20.

Bendavid, J. "Emergence of National Traditions in the Sociology of Science - United-States and Great-Britain." Sociological Inquiry 48, no. 3-4 (1978): 197-218.

———. The Scientist’s Role in Society: A Comparative Study. Chicago: University of Chicago Press, 1971.

———. "Sociology of Science - Introduction." International Social Science Journal 22, no. 1 (1970): 7-27.

Bendavid, J., and T. A. Sullivan. "Sociology of Science." Annual Review of Sociology 1 (1975): 203-22.

Berg, M. "Practices of Reading and Writing: The Constitutive Role of the Patient Record in Medical Work." Sociology of Health & Illness 18, no. 4 (Sep 1996): 499-524.

Berg, M., K. Horstman, S. Plass, and M. van Heusden. "Guidelines, Professionals and the Production of Objectivity: Standardisation and the Professionalism of Insurance Medicine." Sociology of Health & Illness 22, no. 6 (Nov 2000): 765-91.

Berger, P. "Identity as a Problem in Sociology of Knowledge." Archives Europeennes De Sociologie 7, no. 1 (1966): 105-15.

Berger, P. , and T. Luckmann. The Social Construction of Reality: A Treatise in the Sociology of

Knowledge. Garden City: Doubleday, 1966.

Black, A. "Information History." Annual Review of Information Science and Technology 40 (2006): 441-73.

Bloor, D. "Ordinary Human Inference as Material for the Sociology of Knowledge." Social Studies of Science 22, no. 1 (Feb 1992): 129-39.

———. "Knowledge and Reflexivity - New Frontiers in the Sociology of Knowledge - Woolgar,S." Isis 81, no. 306 (Mar 1990): 155-56.

———. "Primitive Classification and the Sociology of Knowledge - Reply to Smith,J.W." Studies in History and Philosophy of Science 15, no. 3 (1984): 245-49.

———. "Durkheim and Mauss Revisited - Classification and the Sociology of Knowledge." Studies in History and Philosophy of Science 13, no. 4 (1982): 267-97.

———."Classification and the Sociology of Knowledge - a New Look at Durkheim and Mauss." Kolner Zeitschrift Fur Soziologie Und Sozialpsychologie (1980): 20-51.

———. Knowledge and Social Imagery. Boston: Routledge and Kegan Paul, 1976.

Blute, Marion. Darwinian Sociocultural Evolution: Solutions to Dilemmas in Cultural and Social Theory. Cambridge Cambridge University Press, 2010.

———. "History Versus Science: The Evolutionary Solution." The Canadian Journal of Sociology 22, no. 3 (1997): 345-64.

———. "Reflections on Trees of Knowledge." Spontaneous Generations: A Journal for the History and Philosophy of Science 3, no. 1 (2009): 223-25.

Blute, Marion, and Paul Armstrong. "The Reinvention of Grand Theories of the Scientific/Scholarly Process." Perspectives on Science 19 (2011): 391-425.

Bonaccorsi, Andrea, and Juan Vargas. "Proliferation Dynamics in New Sciences." Research Policy 39 (2010): 1034-50.

Bryant, Joseph. "An Evolutionary Social Science? A Skeptic’s Brief, Theoretical and Substantive." Philosophy of the Social Sciences 34, no. 4 (2004): 451-92.

Bunge, Mario. Chasing Reality: Strife over Realism. Toronto: University of Toronto Press, 2006.

———. Emergence and Convergence: Qualitative Novelty and the Unity of Knowledge. Toronto: University of Toronto Press, 2003.

Callon, Michel. "Four Models for the Dynamics of Science." In Handbook of Science and Technology Studies, edited by Sheila Jasanoff, Gerald E. Markle, James C. Petersen, and Trevor Pinch. 29-63: Sage, 2001.

Campbell, D. G. "The Author Responds: Popper and Selection Theory." Social Epistemology: A Journal of Knowledge, Culture and Policy 2, no. 4 (1988): 371-77.

———."Blind Variation and Selective Retention in Creative Thought as in Other Knowledge Processes." Psychological Review 67, no. 6 (1960): 380-400.

Campbell, D. T., P. Chapin, E. F. Infante, O. Larsen, J. Young, C. Kruytbosch, A. Morin, H. T. Huang, and S. Cozzens. "Toward an Epistemologically-Relevant Sociology of Science." Science Technology & Human Values, no. 50 (1985): 38-48.

Clark, T. N., W. Kornblum, H. Bloom, and S. Tobias. "Discipline, Method, Community Structure, and Decision-Making - Role and Limitations of Sociology of Knowledge." American Sociologist 3, no. 3 (1968): 214-17.

Clemens, E. S., W. W. Powell, K. McIlwaine, and D. Okamoto. "Careers in Print - Books, Journals, and Scholarly Reputations." American Journal of Sociology 101, no. 2 (Sep 1995): 433-94.

Cohen, I. B. Social-Studies of Science - Barber,B. Current Contents. Vol. 9,1992.

Cole, S. Making Science: Between Nature and Society. Cambridge: Harvard University Press, 1992.

Collins, H. M. Changing Order: Replication and Induction in Scientific Practice. Thousand Oaks: Sage, 1985.

Collins, Randall. The Sociology of Philosophies: A Global Theory of Intellectual Change. Cambridge: Harvard University Press, 1998.

Collins, R., and S. Restivo. "Development, Diversity, and Conflict in the Sociology of Science." Sociological Quarterly 24, no. 2 (1983): 185-200.

Colp, Ralph. Jr. "The Myth of the Darwin-Marx Letter." History of Political Economy 14, no. 4 (1982): 461-82.

Cotgrove, S. "Sociology of Science and Technology." British Journal of Sociology 21, no. 1 (1970): 1-15.

Coulter, N., I. Monarch, and S. Konda. "Software Engineering as Seen through Its Research Literature: A Study in Co-Word Analysis." Journal of the American Society for Information Science 49, no. 13 (Nov 1998): 1206-23.

Currie, Thomas E, Simon J. Greenhill, Russell D. Gray, Toshikazu Hasegawa, and Ruth Mace. "Rise and Fall of Political Complexity in Island South-East Asia and the Pacific." Nature 467 (2010): 801-04.

Curtis, J. E., and J. W. Petras. "Community Power, Power Studies and Sociology of Knowledge." Human Organization 29, no. 3 (1970): 204-18.

Delamont, S. "3 Blind Spots - a Comment on the Sociology of Science by a Puzzled Outsider." Social Studies of Science 17, no. 1 (Feb 1987): 163-70.

Demeritt, D. "The Construction of Global Warming and the Politics of Science." Annals of the Association of American Geographers 91, no. 2 (Jun 2001): 307-37.

———. "Social Theory and the Reconstruction of Science and Geography." Transactions of the Institute of British Geographers 21, no. 3 (1996): 484-503.

Devettere, Raymond J. "Human Understanding. Volume 1.". International Philosophical

Quarterly 13, no. 3 (1973): 449-52.

Ding, Y., G. G. Chowdhury, and S. Foo. "Bibliometric Cartography of Information Retrieval Research by Using Co-Word Analysis." Information Processing & Management 37, no. 6 (Nov 2001): 817-42.

———. "Incorporating the Results of Co-Word Analyses to Increase Search Variety for Information Retrieval." Journal of Information Science 26, no. 6 (2000): 429-51.

Dolby, R. G. A. "Sociology of Knowledge in Natural Science." Science Studies 1, no. 1 (1971): 3-&.

Domhoff, G. W. "Corporate-Liberal Theory and the Social-Security Act - a Chapter in the Sociology of Knowledge." Politics & Society 15, no. 3 (1986): 297-330.

Drori, Gili, John Meyer, Francisco Ramirez, and Even Schofer. Science in the Modern World Polity: Institutionalization and Globalization. Stanford: Stanford University Press, 2003.

Fallis, D. "Epistemic Value Theory and Information Ethics." Minds and Machines 14, no. 1 (Feb 2004): 101-17.

———. "Social Epistemology and Information Science." Annual Review of Information Science and Technology 40 (2006): 475-519.

———. "Social Epistemology and Lis: How to Clarify Our Epistemic Objectives." Canadian Journal of Information and Library Science-Revue Canadienne Des Sciences De L Information Et De Bibliotheconomie 25, no. 4 (Dec 2000): 42-42.

———. Social Epistemology and Lis: How to Clarify Our Epistemic Objectives. Beyond the Web: Technologies, Knowledge and People. edited by D. G. Campbell2001.

Fallis, D., and D. Whitcomb. "Epistemic Values and Information Management." Information Society 25, no. 3 (2009): 175-89.

Feuer, Lewis S. "The Case of the “Darwin-Marx” Letter: A Study in Socio-Literary Detection." Encounter (October 1978): 62-67.

Feyerabend, P. Against Method. Brooklyn: Verso, 1975.

———. Science in a Free Society. New York: Schocken, 1978.

Fischer, G. "Current Soviet Work in Sociology - Note in Sociology of Knowledge." American Sociologist 1, no. 3 (1966): 127-32.

Frickel, Scott , and Neil Gross. "A General Theory of Scientific/Intellectual Movements." American Sociological Review 70 (2005): 204-32.

Fuller, Steve. The Philosophy of Science and Technology Studies. New York: Routledge, 2006.

Garfield, E. "Additional History and Sociology of Science Coverage in Current Contents." Current Contents, no. 50 (1978): 5-8.

———. "Bernal,J.D. The Sage of Cambridge - 4s Award Memorializes His Contributions to the Social-Studies of Science." Current Contents, no. 19 (1982): 5-17.

———. "Citation Indexing, Historio-Bibliography, and Sociology of Science." Current Contents/Life Sciences 14, no. 15 (1971): M25-&.

———. "Cocitation Analysis of the Scientific Literature - Small,Henry on Mapping the Collective Mind of Science - an Introduction to Macrolevel Changes in the Structure of Cocitation Clusters - 1983-1989 by Small,Henry (Reprinted from Scientometrics, Vol 26, Pg 5-20, 1993)." Current Contents 19 (May 1993): 3-13.

———. "From Citation Indexes to Informetrics: Is the Tail Now Wagging the Dog?". Libri 48, no. 2 (Jun 1998): 67-80.

———. "From the Science of Science to Scientometrics Visualizing the History of Science with Histcite Software." Journal of Informetrics 3, no. 3 (Jul 2009): 173-79.

———. From the Science of Science to Scientometrics. Visualizing the History of Science with Histcite Software. Proceedings of Issi 2007: 11th International Conference of the International Society for Scientometrics and Informetrics, Vols I and Ii. edited by D. TorresSalinas and H. F. Moed2007.

———. "Historiographic Mapping of Knowledge Domains Literature." Journal of Information Science 30, no. 2 (2004): 119-45.

———. "The Intended Consequences of Robert K. Merton." Scientometrics 60, no. 1 (2004): 51- 61.

———. "Kranzberg,Mel Receives Bernal Prize as Pioneering Historian of Technology - an Introduction to Acceptance Speech of Kranzberg,Melvin - Jd-Bernal-Prize (1991) - Society-for-Social-Studies-of-Science, Cambridge, Ma, Nov 14-17, 1991 by Kranzberg,Melvin." Current Contents 13 (Mar 1992): 3-9.

———. "Measuring R-and-D Productivity through Scientometrics." Current Contents, no. 30 (Jul 1988): 3-9.

———. "New International Professional Society Signals the Maturing of Scientometrics and Informetrics." Scientist 9, no. 16 (Aug 1995): 11-11.

———. "Price,Derek and the Practical World of Scientometrics." Science Technology & Human Values 13, no. 3-4 (Sum-Fal 1988): 349-50.

———. "Science Historian Cohen,I.B. Reviews Social-Studies of Science by Sociologist Barber,Bernard - an Introduction to Social-Studies of Science - Barber,B. By Cohen,I.B." Current Contents 9 (Mar 1992): 3-7.

———. "Scientometrics Comes of Age." Current Contents, no. 46 (1979): 5-10.

Garfield, E., A. I. Pudovkin, and S. W. Paris. "A Bibliometric and Historiographic Analysis of the Work of Tony Van Raan: A Tribute to a Scientometrics Pioneer and Gatekeeper." Research Evaluation 19, no. 3 (Sep 2010): 161-72.

Garfield, E., and A. Welljamsdorof. "The Microbiology Literature - Languages of Publication and Their Relative Citation Impact." Fems Microbiology Letters 100, no. 1-3 (Dec 1992): 33-37.

———. "The Microbiology Literature - Languages of Publication and Their Relative Citation Impact (Reprinted from Fems Microbiology Letters, Vol 100, Pg 33-37, 1992)." Current

Contents 47 (Nov 1992): 6-10.

———. "Of Nobel Class - a Citation Perspective on High-Impact Research Authors." Theoretical Medicine 13, no. 2 (Jun 1992): 117-35.

———. "Of Nobel Class - a Citation Perspective on High-Impact Research Authors (Reprinted from Theoretical Medicine, Vol 13, 1992)." Current Contents 33 (Aug 1992): 5-13.

Gherardi, S. "Learning as Problem-Driven or Learning in the Face of Mystery?". Organization Studies 20, no. 1 (1999): 101-23.

Ghiselin, Michael T. "A Radical Solution to the Species Problem." Systematic Zoology 23 (1975): 536-44.

Giere, Ronald N. Scientific Perspectivism. Chicago: University of Chicago Press, 2006.

Godfrey-Smith, Peter. "David Hull". Biology and Philosophy 25, no. 5 (2010): 749-53.

———. "Conditions for Evolution by Natural Selection." The Journal of Philosophy 104, no. 10 (2007): 489-516.

Goldman, A. "Social Epistemology, Theory of Evidence, and Intelligent Design: Deciding What to Teach." Southern Journal of Philosophy 44 (2006): 1-22.

Goldman, A., and A. Grinstein. "Stages in the Development of Market Orientation Publication Activity a Longitudinal Assessment." European Journal of Marketing 44, no. 9-10 (2010): 1384-409.

Goldman, A. I. "Argumentation and Social Epistemology." Journal of Philosophy 91, no. 1 (Jan 1994): 27-49.

———. "Social Epistemology." Critica-Revista Hispanoamericana De Filosofia 31, no. 93 (Dec 1999): 3-19.

———. Veritistic Social Epistemology. Proceedings of the Twentieth World Congress of Philosophy, Vol 5: Epistemology. edited by R. CobbStevens2000.

Hagstrom, W. O. The Scientific Community. New York: Basic Books, 1965.

Harvey, L. "The Nature of Schools in the Sociology of Knowledge - the Case of the Chicago- School." Sociological Review 35, no. 2 (May 1987): 245-78.

———. "The Use and Abuse of Kuhnian Paradigms in the Sociology of Knowledge." Sociology- the Journal of the British Sociological Association 16, no. 1 (1982): 85-107.

Hess, David. Science Studies: An Advanced Introduction. New York: New York University Press, 1997.

Holzner, B. "Special Issue - the Sociology of Knowledge - Introduction." Knowledge-Creation Diffusion Utilization 4, no. 1 (1982): 3-6.

Holzner, B., and E. Fisher. "Knowledge in Use - Considerations in the Sociology of Knowledge Application." Knowledge-Creation Diffusion Utilization 1, no. 2 (1979): 219-44.

Hufford, D. J. "The Supernatural and the Sociology of Knowledge, Explaining Academic Belief." New York Folklore 9, no. 1-2 (1983): 21-29.

Hull, David. "The Use and Abuse of Sir Karl Popper." Biology and Philosophy 14 (1999): 481- 504.

———. "A Mechanism and Its Metaphysics: An Evolutionary Account of the Social and Conceptual Development of Science." Biology and Philosophy 3 (1988): 123-55.

———. Science as a Process: An Evolutionary Account of the Social and Conceptual Development of Science. Chicago: University of Chicago Press, 1988.

Hussey, Trevor. "Evolutionary Change and Epistemology." Biology and Philosophy 14, no. 4 (1999): 561-84.

Keller, Evelyn Fox. Reflections on Gender and Science. Yale University Press, 1985.

Kitcher, P. "Reviving the Sociology of Science." Philosophy of Science (Proceedings) 67 (2000): S33-S44.

Knorr-Centina, Karin. The Manufacture of Knowledge - an Essay on the Constructivist and Contextual Nature of Science. Oxford: Pergamon Press, 1981.

Kuklick, H. "The Sociology of Knowledge - Retrospect and Prospect." Annual Review of Sociology 9 (1983): 287-310.

Kuhn, Thomas S. The Structure of Scientific Revolutions. Chicago: University of Chicago Press, 1962.

Lakatos, I. "Falsification and the Methodology of Scientific Research Programmes." In In Criticism and the Growth of Knowledge, edited by I. Lakatos and A. Musgrave. Cambridge: Cambridge University Press, 1970.

Latour, B. "Gabriel Tarde and the End of the Social." Soziale Welt-Zeitschrift Fur Sozialwissenschaftliche Forschung Und Praxis 52, no. 3 (2001): 361-+.

———. "Is Re-Modernization Occurring - and If So, How to Prove It? A Commentary on Ulrich Beck." Theory Culture & Society 20, no. 2 (Apr 2003): 35-+.

———. "When Things Strike Back: A Possible Contribution of 'Science Studies' to the Social Sciences." British Journal of Sociology 51, no. 1 (Jan-Mar 2000): 107-23.

Latour, Bruno , and Steve Woolgar. Laboratory Life: The Social Construction of Scientific Facts. Sage Publications Inc., 1979.

Law, J. "Theories and Methods in Sociology of Science - Interpretive Approach." Social Science Information Sur Les Sciences Sociales 13, no. 4-5 (1974): 163-72.

Lenoir, T. "Perspectives in the Sociology of Science - Blume,Ss." Isis 70, no. 251 (1979): 152- 53.

———. "Quantitative Foundations for the Sociology of Science - Linking Blockmodeling with Co-Citation Analysis." Social Studies of Science 9, no. 4 (1979): 455-80.

———. "Sociology of Science - Studies and Materials - German - Stehr,N, Konig,R." Isis 70, no. 253 (1979): 447-48.

———. "Sociology of Science in Europe - Merton,Rk, Gaston,J." Isis 70, no. 251 (1979): 152- 53.

Lewontin, Richard C. "Gene, Organism and Environment." In Evolution from Molecules to Men, edited by D.S. Bendall. Cambridge: Cambridge University Press, 1983.

Leydesdorff, L. "Caveats for the Use of Citation Indicators in Research and Journal Evaluations." Journal of the American Society for Information Science and Technology 59, no. 2 (Jan 2008): 278-87.

———. "Configurational Information as Potentially Negative Entropy: The Triple Helix Model." Entropy 10, no. 4 (Dec 2008): 391-410.

———. "Environment and Planning B: Planning and Design as a Journal: The Interdisciplinarity of Its Environment and the Citation Impact." Environment and Planning B-Planning & Design 34, no. 5 (Sep 2007): 826-38.

———. "Evaluation of Research and Evolution of Science Indicators." Current Science 89, no. 9 (Nov 2005): 1510-17.

———. "Is Society a Self-Organizing System." Journal of Social and Evolutionary Systems 16, no. 3 (1993): 331-49.

———. "Mapping Interdisciplinarity at the Interfaces between the Science Citation Index and the Social Science Citation Index." Scientometrics 71, no. 3 (Jun 2007): 391-405.

———. "'Meaning' as a Sociological Concept: A Review of the Modeling, Mapping and Simulation of the Communication of Knowledge and Meaning." Social Science Information Sur Les Sciences Sociales 50, no. 3-4 (Sep-Dec 2011): 391-413.

———. "The Relations between Qualitative Theory and Scientometric Methods in Science and Technology Studies - Introduction to the Topical Issue." Scientometrics 15, no. 5-6 (May 1989): 333-47.

———. "Scientometrics - French - Callon,M, Courtial,Jp, Penan,H." Scientometrics 30, no. 2-3 (Jun-Aug 1994): 539-41.

———. "Theories of Citation?". Scientometrics 43, no. 1 (Sep 1998): 5-25.

Leydesdorff, L., and M. Meyer. "The Scientometrics of a Triple Helix of University-Industry- Government Relations - (Introduction to the Topical Issue)." Scientometrics 70, no. 2 (Feb 2007): 207-22.

Leydesdorff, L., and I. Rafols. "A Global Map of Science Based on the Isi Subject Categories." Journal of the American Society for Information Science and Technology 60, no. 2 (Feb 2009): 348-62.

Leydesdorff, L., and J. C. Shin. "How to Evaluate Universities in Terms of Their Relative Citation Impacts: Fractional Counting of Citations and the Normalization of Differences among Disciplines." Journal of the American Society for Information Science and Technology 62, no. 6 (Jun 2011): 1146-55.

Leydesdorff, L., and P. Van den Besselaar. "Scientometrics and Communication Theory: Towards Theoretically Informed Indicators." Scientometrics 38, no. 1 (Jan 1997): 155-74.

Leydesdorff, L., and C. S. Wagner. "International Collaboration in Science and the Formation of a Core Group." Journal of Informetrics 2, no. 4 (Oct 2008): 317-25.

Leydesdorff, L., and P. Wouters. "Between Texts and Contexts: Advances in Theories of Citation? (a Rejoinder)." Scientometrics 44, no. 2 (Feb 1999): 169-82.

Lynch, Michael. Art and Artifact in Laboratory Science: A Study of Shop Work and Shop Talk in a Research Laboratory. London: Routledge and Kegan, 1985.

———. Scientific Practice and Ordinary Action: Ethnomethodology and Social Studies of Science. New York: Cambridge University Press, 1985.

Lyon, W. S. "Scientometrics with Some Emphasis on Communication at Scientific Meetings and through the Invisible College." Abstracts of Papers of the American Chemical Society 189, no. APR- (1985): 4-CINF.

———. "Scientometrics with Some Emphasis on Communication at Scientific Meetings and through the Invisible College." Journal of Chemical Information and Computer Sciences

26, no. 2 (May 1986): 47-52.

———. "Social-Studies of Science and Us .1." Journal of Radioanalytical and Nuclear Chemistry 85, no. 3 (1984): 131-35.

———. "Social-Studies of Science and Us .2." Journal of Radioanalytical and Nuclear Chemistry 85, no. 5 (1984): 261-65.

Manis, J. G. "Sociology of Knowledge and Community Mental Health Research." Social Problems 15, no. 4 (1968): 488-501.

Marcus, G. E. "Ethnography in/of the World-System - the Emergence of Multi-Sited Ethnography." Annual Review of Anthropology 24 (1995): 95-117.

Mayr, Ernst. " Darwin’s Principle of Divergence." Journal of the History of Biology 25, no. 3 (1992): 343-59.

———. Toward a New Philosophy of Biology: Observations of an Evolutionist. Cambridge: Belknap Press/Harvard University Press, 1988.

Mayr, Ernst, and W.J. Bock. "Classification and Other Ordering Systems." Journal of Zoological Systematics and Evolutionary Research 40, no. 4 (2002): 169-94.

McCain, Katherine W. "Longitudinal Author Cocitation Mapping: The Changing Structure of Macroeconomics." Journal of the American Society for Information Science 35 (1984): 351-59.

McCain, Katherine W. "Mapping Authors in Intellectual Space: A Technical Overview." Journal of the American Society for Information Science 41 (1990): 433-43.

———. "The Paper Trails of Scholarship: Mapping the Literature of Genetics." Library Quarterly 56 (1986): 258-71.

McKinley, R. "Why Do Crow and Omaha Kinship Terminologies Exist - Sociology of Knowledge Interpretation." Man 6, no. 3 (1971): 408-26.

Merton, R. K. . "On the Garfield Input to the Sociology of Science: A Retrospective Collage." In

The Web of Knowledge: A Festschrift in Honor of Eugene Garfield, edited by B. Cronin and H.B. Atkins, 435-48. Medford: Information Today, 2000.

———. "Insiders and Outsiders - Chapter in Sociology of Knowledge." American Journal of Sociology 78, no. 1 (1972): 9-&.

———. "The Normative Structure of Science." In The Sociology of Science: Theoretical and Empirical Investigations, edited by N. W. Storer. Chicago: University of Chicago Press, 1942.

———. "The Sociology of Knowledge." Isis 27 (1937): 493–503.

Merton, R. K. , and N.W. Storer, eds. The Sociology of Science: Theoretical and Empirical Investigations. Chicago: University of Chicago Press, 1973.

Meyer, M. "What Is Special About Patent Citations? Differences between Scientific and Patent Citations." Scientometrics 49, no. 1 (Sep 2000): 93-123.

Meyer, M., T. S. Pereira, O. Persson, and O. Granstrand. "The Scientornetric World of Keith Pavitt - a Tribute to His Contributions to Research Policy and Patent Analysis." Research Policy 33, no. 9 (Nov 2004): 1405-17.

Meyer, M., M. A. Zaggl, and K. M. Carley. "Measuring Cmot's Intellectual Structure and Its Development." Computational and Mathematical Organization Theory 17, no. 1 (Mar 2011): 1-34.

Moody, J. "The Structure of a Social Science Collaboration Network: Disciplinary Cohesion from 1963 to 1999." American Sociological Review 69, no. 2 (Apr 2004): 213-38.

Moravcsik, M. J. "Applied Scientometrics - an Assessment Methodology for Developing- Countries." Scientometrics 7, no. 3-6 (1985): 165-76.

Mulkay, M. "Action and Belief or Scientific Discourse - a Possible Way of Ending Intellectual Vassalage in Social-Studies of Science." Philosophy of the Social Sciences 11, no. 2 (1981): 163-71.

———. "Knowing and Using - Implications for the Sociology of Knowledge." Kolner Zeitschrift Fur Soziologie Und Sozialpsychologie (1980): 52-72.

———. "Knowledge and Utility - Implications for the Sociology of Knowledge." Social Studies of Science 9, no. 1 (1979): 63-80.

———. "The Sociology of Science in East and West .1. Sociology of Science in the West." Current Sociology-Sociologie Contemporaine 28, no. 3 (1980): 1-&.

———. "Methodology in Sociology of Science - Reflections on Study of Radio Astronomy." Social Science Information Sur Les Sciences Sociales 13, no. 2 (1974): 107-19.

Murdoch, J. "Inhuman/Nonhuman/Human: Actor-Network Theory and the Prospects for a Nondualistic and Symmetrical Perspective on Nature and Society." Environment and Planning D-Society & Space 15, no. 6 (Dec 1997): 731-56.

Nakhaie, M. R. "Universalism, Ascription and Academic Rank: Canadian Professors, 1987- 2000." The Canadian Review of Sociology and Anthropology 44 (2007): 361-86.

Nettleton, S., and R. Burrows. "E-Scaped Medicine? Information, Reflexivity and Health." Critical Social Policy 23, no. 2 (May 2003): 165-85.

Nowotny, Helga. "Human Understanding by Stephen Toulmin." Theory and Society 1, no. 3 (1974): 382-85.

Popper, Karl. Conjectures and Refutations : The Growth of Scientific Knowledge. London: Routledge, 1959.

———. Objective Knowledge: An Evolutionary Approach. Oxford: Oxford University Press, 1972.

———. The Logic of Scientific Discovery. New York Basic Books, 1959.

Price, Derek J. de Solla. Little Science, Big Science. New York: Columbia University Press, 1963.

Quine, W. "Two Dogmas of Empiricism." The Philosophical Review 60 (1951): 20-43.

———. Word and Object. Cambridge: MIT Press, 1960.

Restivo, S. "Commentary - Some Perspectives in Contemporary Sociology of Science." Science Technology & Human Values, no. 35 (1981): 22-30.

———. "Mathematics and the Limits of the Sociology of Knowledge." Social Science Information Sur Les Sciences Sociales 20, no. 4-5 (1981): 679-701.

———. "Mathematics and the Sociology of Knowledge." Knowledge-Creation Diffusion Utilization 4, no. 1 (1982): 127-44.

———. "Science and Technology Studies-Toronto 80, the Joint Annual-Meetings of the History-of-Science-Society, Philosophy-of-Science-Association, Society-for-the-History- of-Technology, and Society-for-the-Social-Studies-of-Science, Held in Toronto, Canada, 16-19 October 1980." Science Technology & Human Values, no. 34 (1981): 20-24.

———. "Science and the Sociology of Knowledge - Mulkay,M." American Journal of Sociology 88, no. 1 (1982): 207-08.

———. "Social-Studies of Science - Barber,B." Contemporary Sociology-a Journal of Reviews 20, no. 1 (Jan 1991): 108-09.

Restivo, S., and J. Loughlin. "Critical-Sociology of Science and Scientific Validity." Knowledge- Creation Diffusion Utilization 8, no. 3 (Mar 1987): 486-508.

Restivo, S. , and J. Croissant. "Social Constructionism in Science and Technology Studies." In In Handbook of Constructionist Research, edited by J. A. Holstein and J. F. Gubrium, 213- 30. New York: The Guilford Press, 2008.

Richards, E., and J. Schuster. "The Feminine Method as Myth and Accounting Resource - a Challenge to Gender Studies and Social-Studies of Science." Social Studies of Science 19, no. 4 (Nov 1989): 697-720.

Rollhansen, N. "The Controversy between Biometricians and Mendelians - a Test Case for the Sociology of Scientific Knowledge." Social Science Information Sur Les Sciences Sociales 19, no. 3 (1980): 501-17.

Rossiter, M. W. "The Matthew-Matilda Effect in Science." Social Studies of Science 23, no. 2 (May 1993): 325-41.

Roth, W. M. "Situated Cognition and Assessment of Competence in Science." Evaluation and Program Planning 21, no. 2 (May 1998): 155-69.

Roth, W. M., and K. B. Lucas. "From ''Truth'' to ''Invented Reality'': A Discourse Analysis of High School Physics Students' Talk About Scientific Knowledge." Journal of Research in Science Teaching 34, no. 2 (Feb 1997): 145-79.

Roth, W. M., and M. K. McGinn. "Inscriptions: Toward a Theory of Representing as Social Practice." Review of Educational Research 68, no. 1 (Spr 1998): 35-59.

———. "Knowing, Researching, and Reporting Science Education: Lessons from Science and Technology Studies." Journal of Research in Science Teaching 35, no. 2 (Feb 1998): 213-35.

Roth, W. M., M. K. McGinn, and G. M. Bowen. "Applications of Science and Technology Studies: Effecting Change in Science Education." Science Technology & Human Values 21, no. 4 (Fal 1996): 454-84.

Salamini, L. "Gramsci and Marxist Sociology of Knowledge - Analysis of Hegemony - Ideology - Knowledge." Sociological Quarterly 15, no. 3 (1974): 359-80.

Shapin, S. "Here and Everywhere - Sociology of Scientific Knowledge." Annual Review of Sociology 21 (1995): 289-321.

Shwed, Uri , and Peter S. Bearman. "The Temporal Structure of Scientific Consensus Formation." American Sociological Review 75 (2010): 817-40.

Siler, K. S. , and N. McLaughlin. "The Canada Research Chairs Program and Social Science Reward Structures." The Canadian Review of Sociology 45 (2008): 93-119.

Sismondo, Sergio. "Science and Technology Studies and an Engaged Program." In The Handbook of Science and Technology Studies of Science and Technology Studies, edited by E.J. Hackett, Amsterdamska, O., Lynch, M. And Wajcman, J. . 13-31: The MIT Press,

2008.

Slocum, R. "Polar Bears and Energy-Efficient Lightbulbs: Strategies to Bring Climate Change Home." Environment and Planning D-Society & Space 22, no. 3 (Jun 2004): 413-38.

Smith, J. W. "Primitive Classification and the Sociology of Knowledge - a Response to Bloor." Studies in History and Philosophy of Science 15, no. 3 (1984): 237-43.

Sokal, R.R., and P. Sneath. Principles of Numerical Taxonomy. San Francisco: W.H. Freeman, 1963.

Straus, M. A. "Bucking the Tide in Family Violence Research." Trauma Violence & Abuse 9, no. 4 (Oct 2008): 191-213.

———. The Controversy over Domestic Violence by Women - a Methodological, Theoretical, and Sociology of Science Analysis. Violence in Intimate Relationships. edited by X. B. Arriaga and S. Oskamp1999.

Sullivan, T. A. "Sociology of Science in Europe - Merton,Rk, Gaston,J." American Journal of Sociology 85, no. 3 (1979): 678-80.

Toulmin, Stephen E. . "The Evolutionary Development of Natural Science." American Scientist 55, no. 4 (1967): 456-71.

———. Human Understanding: The Collective Use and Evolution of Concepts. Princeton: Princeton University Press, 1972.

Tibbetts, P. "The Sociology of Scientific Knowledge - the Constructivist Thesis and Relativism." Philosophy of the Social Sciences 16, no. 1 (Mar 1986): 39-57.

Twain, M. . "Report of My Death." http://twainquotes.com/Death.html.

Uebel, T. E. "Logical Empiricism and the Sociology of Knowledge: The Case of Neurath and Frank." Philosophy of Science 67, no. 3 (Sep 2000): S138-S50.

Vinkler, P. "An Attempt for Defining Some Basic Categories of Scientometrics and Classifying the Indicators of Evaluative Scientometrics." Scientometrics 50, no. 3 (Mar-Apr 2001):

539-44.

———. "Characterization of the Impact of Sets of Scientific Papers: The Garfield (Impact) Factor." Journal of the American Society for Information Science and Technology 55, no. 5 (Mar 2004): 431-35.

———. "Eminence of Scientists in the Light of the H-Index and Other Scientometric Indicators." Journal of Information Science 33, no. 4 (2007): 481-91.

———. "Evaluation of the Publication Activity of Research Teams by Means of Scientometric Indicators." Current Science 79, no. 5 (Sep 2000): 602-12.

———. "Indicators Are the Essence of Scientometrics and Bibliometrics." Scientometrics 85, no. 3 (Dec 2010): 861-66.

———. "Scientometrics and Scientometricians in 2011." Journal of the American Society for Information Science and Technology 62, no. 7 (Jul 2011): 1430-32.

———. "Some Practical Aspects of the Standardization of Scientometric Indicators." Scientometrics 35, no. 2 (Feb 1996): 237-45.

———. "Words and Indicators - as Scientometrics Stands." Scientometrics 30, no. 2-3 (Jun-Aug 1994): 495-504.

Walsh, Denis. "Evolutionary Essentialism." British Journal of the Philosophy of Science 57 (2006): 425-48.

Walton, J. "Discipline, Method, and Community Power - Note on Sociology of Knowledge." American Sociological Review 31, no. 5 (1966): 684-89.

Weiss, R. M., and L. E. Miller. "The Concept of Ideology in Organizational Analysis - the Sociology of Knowledge or the Social-Psychology of Beliefs." Academy of Management Review 12, no. 1 (Jan 1987): 104-16.

Whatmore, S. "Materialist Returns: Practising Cultural Geography in and for a More-Than- Human World." Cultural Geographies 13, no. 4 (Oct 2006): 600-09.

Whatmore, S., and L. Thorne. "Elephants on the Move: Spatial Formations of Wildlife Exchange." Environment and Planning D-Society & Space 18, no. 2 (Apr 2000): 185- 203.

Whatmore, S. J. "Mapping Knowledge Controversies: Science, Democracy and the Redistribution of Expertise." Progress in Human Geography 33, no. 5 (Oct 2009): 587- 98.

White, H. D. "Author Co-Citation Analysis: Overview and Defense." In Scholarly Communication and Bibliometrics, edited by C.L. Borgman. 84-106. Newbury Park: Sage, 1990.

———. "Introduction." Journal of the American Society for Information Science 41 (1990): 430- 31.

White, H. D. & Griffith, B.C. "Author Cocitation: A Literature Measure of Intellectual Structure." Journal of the American Society for Information Science 32 (1981): 163-71.

Winner, L. "Conflicting Interests in Science and Technology Studies - Some Personal Reflections." Technology in Society 11, no. 4 (1989): 433-38.

———. "On the Foundations of Science and Technology Studies." Bulletin of Science Technology & Society 6, no. 2-3 (1986): 219-21.

Woolgar, S. "The Turn to Technology in Social-Studies of Science." Science Technology & Human Values 13, no. 1-2 (Win-Spr 1988): 203-04.

———. "The Turn to Technology in Social-Studies of Science." Science Technology & Human Values 16, no. 1 (Win 1991): 20-50.

———. "The Very Idea of Social Epistemology + Fuller,Steve - What Prospects for a Truly Radical Radically Naturalized Epistemology." Inquiry-an Interdisciplinary Journal of Philosophy 34, no. 3 (Sep 1991): 377-89.

———. "What Is the Analysis of Scientific Rhetoric for - a Comment on the Possible Convergence between Rhetorical Analysis and Social-Studies of Science." Science

Technology & Human Values 14, no. 1 (Win 1989): 47-49.

Wray, Brad K. Kuhn’s Evolutionary Social Epistemology. Cambridge: Cambridge University Press, 2011.

Yablonsky, A. I. "Stable Non-Gaussian Distributions in Scientometrics." Scientometrics 7, no. 3- 6 (1985): 459-70.

Yearley, Steven. Making Sense of Science: Understanding the Social Studies of Science. Sage Publications Ltd, 2005.

Young, M. F. D. "Taking Sides against Probable Problems of Relativism and Commitment in Teaching and Sociology of Knowledge." Educational Review 25, no. 3 (1973): 210-22.

Ziman, John. Real Science: What It Is and What It Means. Cambridge: Cambridge University Press, 2000.