The Rhetoric of Research Methodology Jason Grossman and Joan Leach
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Utilitarianism & the Afterlife
Utilitarianism & the Afterlife The paradox of a pleasant hereafter Betsy McCall The goal of Utilitarianism is to lay out a moral philosophy to provide us a way of living, and a way of making difficult moral choices correctly(Mill, 2001) in circumstances which are uncommon enough that experience has not, or cannot, prepare us for the solution. But in doing so, Utilitarianism must confront the same moral challenges confronted by all moral philosophies, including the consequences of belief in the afterlife(Hasker, 2005). The afterlife has provided a complex moral challenge for many moral philosophical frameworks throughout the ages, from Buddhism to Christianity. Buddhism posits that life is suffering, and that the ultimate goal of living is really to escape living altogether by achieving nirvana, or at least, a better life in the next reincarnation(Becker, 1993). Christianity similarly puts this life into a comparison with another better alternative, in this case, the possibility of an infinitely better afterlife in heaven with god and the angels(Pohle, 1920). In both cases, the philosophical frameworks have been forced to incorporate specific prohibitions against suicide in order to avoid the apparently logical conclusion that death is preferable to life, and we would do well to get ourselves there as quickly as possible. Mill, in arguing for Utilitarianism, does not specifically address this question, perhaps because Mill himself gave the afterlife little personal credence(Wilson, 2009). However, writing to a largely Christian Western audience, like Christianity, and a deep-seated historical affinity for belief in reincarnation(Haraldsson, 2005), Mill and his followers must be prepared to address this potential concern. -
Science Serving the Nation, Impact of Basic Research
Science Serving the Nation The Impact of Basic Research Basic Energy Sciences Science Serving the Nation The Impact of Basic Research Basic Energy Sciences BASIC ENERGY SCIENCES ENERGY ENVIRONMENT SECURITY MATERIALS SCIENCES ENGINEERING CHEMICAL GEOSCIENCES BIOSCIENCES RESEARCH UNDERSTAND PREDICT CONTROL MATTER ENERGY This document highlights the breadth of the scientific programs and the impact of this research in commerce and in advancing scientific frontiers. To look in more detail at our research portfolio and see how we manage our programs, consult our BES 2011 Summary Report, available at http://science.energy.gov/~/media/bes/pdf/reports/files/bes2011sr_rpt.pdf. Additional information about the impact of BES research can be found on our website at http://science.energy.gov/bes/. New highlights showcasing the benefits of BES programs are frequently posted to the web as they arise. Our user facility brochure (http://science.energy.gov/~/media/bes/suf/pdf/BES_Facilities.pdf) contains additional information on all of the BES national scientific user facilities. Energy technologies, environmental tech- nologies, and national security all depend on the progress of basic science research. By understanding, predicting, and ulti- mately controlling matter and energy at the level of electrons, atoms, and molecules, scientists have the capacity to transform the technologies we use in everyday life. From Fundamental Science to Energy Technology Basic Energy Sciences (BES) supports the science that is the foundation for new technologies essential to the U.S. Department of Energy (DOE) missions in energy, environment, and national security. We are one of the Nation’s leading sponsors of fundamental research across broad areas of materials sciences and engineering, chemical sciences, geosciences, and biosciences. -
Would ''Direct Realism'' Resolve the Classical Problem of Induction?
NOU^S 38:2 (2004) 197–232 Would ‘‘Direct Realism’’ Resolve the Classical Problem of Induction? MARC LANGE University of North Carolina at Chapel Hill I Recently, there has been a modest resurgence of interest in the ‘‘Humean’’ problem of induction. For several decades following the recognized failure of Strawsonian ‘‘ordinary-language’’ dissolutions and of Wesley Salmon’s elaboration of Reichenbach’s pragmatic vindication of induction, work on the problem of induction languished. Attention turned instead toward con- firmation theory, as philosophers sensibly tried to understand precisely what it is that a justification of induction should aim to justify. Now, however, in light of Bayesian confirmation theory and other developments in epistemology, several philosophers have begun to reconsider the classical problem of induction. In section 2, I shall review a few of these developments. Though some of them will turn out to be unilluminating, others will profitably suggest that we not meet inductive scepticism by trying to justify some alleged general principle of ampliative reasoning. Accordingly, in section 3, I shall examine how the problem of induction arises in the context of one particular ‘‘inductive leap’’: the confirmation, most famously by Henrietta Leavitt and Harlow Shapley about a century ago, that a period-luminosity relation governs all Cepheid variable stars. This is a good example for the inductive sceptic’s purposes, since it is difficult to see how the sparse background knowledge available at the time could have entitled stellar astronomers to regard their observations as justifying this grand inductive generalization. I shall argue that the observation reports that confirmed the Cepheid period- luminosity law were themselves ‘‘thick’’ with expectations regarding as yet unknown laws of nature. -
Understanding the Value of Arts & Culture | the AHRC Cultural Value
Understanding the value of arts & culture The AHRC Cultural Value Project Geoffrey Crossick & Patrycja Kaszynska 2 Understanding the value of arts & culture The AHRC Cultural Value Project Geoffrey Crossick & Patrycja Kaszynska THE AHRC CULTURAL VALUE PROJECT CONTENTS Foreword 3 4. The engaged citizen: civic agency 58 & civic engagement Executive summary 6 Preconditions for political engagement 59 Civic space and civic engagement: three case studies 61 Part 1 Introduction Creative challenge: cultural industries, digging 63 and climate change 1. Rethinking the terms of the cultural 12 Culture, conflict and post-conflict: 66 value debate a double-edged sword? The Cultural Value Project 12 Culture and art: a brief intellectual history 14 5. Communities, Regeneration and Space 71 Cultural policy and the many lives of cultural value 16 Place, identity and public art 71 Beyond dichotomies: the view from 19 Urban regeneration 74 Cultural Value Project awards Creative places, creative quarters 77 Prioritising experience and methodological diversity 21 Community arts 81 Coda: arts, culture and rural communities 83 2. Cross-cutting themes 25 Modes of cultural engagement 25 6. Economy: impact, innovation and ecology 86 Arts and culture in an unequal society 29 The economic benefits of what? 87 Digital transformations 34 Ways of counting 89 Wellbeing and capabilities 37 Agglomeration and attractiveness 91 The innovation economy 92 Part 2 Components of Cultural Value Ecologies of culture 95 3. The reflective individual 42 7. Health, ageing and wellbeing 100 Cultural engagement and the self 43 Therapeutic, clinical and environmental 101 Case study: arts, culture and the criminal 47 interventions justice system Community-based arts and health 104 Cultural engagement and the other 49 Longer-term health benefits and subjective 106 Case study: professional and informal carers 51 wellbeing Culture and international influence 54 Ageing and dementia 108 Two cultures? 110 8. -
There Is No Pure Empirical Reasoning
There Is No Pure Empirical Reasoning 1. Empiricism and the Question of Empirical Reasons Empiricism may be defined as the view there is no a priori justification for any synthetic claim. Critics object that empiricism cannot account for all the kinds of knowledge we seem to possess, such as moral knowledge, metaphysical knowledge, mathematical knowledge, and modal knowledge.1 In some cases, empiricists try to account for these types of knowledge; in other cases, they shrug off the objections, happily concluding, for example, that there is no moral knowledge, or that there is no metaphysical knowledge.2 But empiricism cannot shrug off just any type of knowledge; to be minimally plausible, empiricism must, for example, at least be able to account for paradigm instances of empirical knowledge, including especially scientific knowledge. Empirical knowledge can be divided into three categories: (a) knowledge by direct observation; (b) knowledge that is deductively inferred from observations; and (c) knowledge that is non-deductively inferred from observations, including knowledge arrived at by induction and inference to the best explanation. Category (c) includes all scientific knowledge. This category is of particular import to empiricists, many of whom take scientific knowledge as a sort of paradigm for knowledge in general; indeed, this forms a central source of motivation for empiricism.3 Thus, if there is any kind of knowledge that empiricists need to be able to account for, it is knowledge of type (c). I use the term “empirical reasoning” to refer to the reasoning involved in acquiring this type of knowledge – that is, to any instance of reasoning in which (i) the premises are justified directly by observation, (ii) the reasoning is non- deductive, and (iii) the reasoning provides adequate justification for the conclusion. -
A Comprehensive Framework to Reinforce Evidence Synthesis Features in Cloud-Based Systematic Review Tools
applied sciences Article A Comprehensive Framework to Reinforce Evidence Synthesis Features in Cloud-Based Systematic Review Tools Tatiana Person 1,* , Iván Ruiz-Rube 1 , José Miguel Mota 1 , Manuel Jesús Cobo 1 , Alexey Tselykh 2 and Juan Manuel Dodero 1 1 Department of Informatics Engineering, University of Cadiz, 11519 Puerto Real, Spain; [email protected] (I.R.-R.); [email protected] (J.M.M.); [email protected] (M.J.C.); [email protected] (J.M.D.) 2 Department of Information and Analytical Security Systems, Institute of Computer Technologies and Information Security, Southern Federal University, 347922 Taganrog, Russia; [email protected] * Correspondence: [email protected] Abstract: Systematic reviews are powerful methods used to determine the state-of-the-art in a given field from existing studies and literature. They are critical but time-consuming in research and decision making for various disciplines. When conducting a review, a large volume of data is usually generated from relevant studies. Computer-based tools are often used to manage such data and to support the systematic review process. This paper describes a comprehensive analysis to gather the required features of a systematic review tool, in order to support the complete evidence synthesis process. We propose a framework, elaborated by consulting experts in different knowledge areas, to evaluate significant features and thus reinforce existing tool capabilities. The framework will be used to enhance the currently available functionality of CloudSERA, a cloud-based systematic review Citation: Person, T.; Ruiz-Rube, I.; Mota, J.M.; Cobo, M.J.; Tselykh, A.; tool focused on Computer Science, to implement evidence-based systematic review processes in Dodero, J.M. -
Measuring Science: Is There “Basic Research” Without Statistics? Benoît Godin 3465, Rue Durocher Montréal (QUE) Canada
Measuring Science: Is There “Basic Research” Without Statistics? Benoît Godin 3465, rue Durocher Montréal (QUE) Canada H2X 2C6 Project on the History and Sociology of S&T Statistics Paper No. 3 2000 Previous papers in the series: 1. B. Godin, Outlines for a History of Science Measurement. 2. B. Godin, The Measure of Science and the Construction of a Statistical Territory: The Case of the National Capital Region (NCR). 1 Measuring Science : Is There “Basic Research” Without Statistics? Measuring Science: Is There “Basic Research” Without Statistics? Fundamental research is a central category of science policy and science measurement. Of all the concepts defined in the first edition of the Frascati manual, the OECD (Organization for Economic and Co-operation Development) methodological guide for official surveys on R&D, the first dealt with fundamental research. While a definition of research itself did not appear until the second edition in 1970, fundamental research was defined explicitly as follows: Work undertaken primarily for the advancement of scientific knowledge, without a specific practical application in view. 1 In the last edition of the manual (1994), the definition is substantially the same as the one in 1963, although the term “basic” is now used instead of fundamental: Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundation of phenomena and observable facts, without any particular application or use in view. 2 Between 1963 and 1994, therefore, all five editions of the manual carry essentially the same definition without any significant changes: basic research is research concerned with knowledge as contrasted with applied research, which is concerned with the application of knowledge. -
Cebm-Levels-Of-Evidence-Background-Document-2-1.Pdf
Background document Explanation of the 2011 Oxford Centre for Evidence-Based Medicine (OCEBM) Levels of Evidence Introduction The OCEBM Levels of Evidence was designed so that in addition to traditional critical appraisal, it can be used as a heuristic that clinicians and patients can use to answer clinical questions quickly and without resorting to pre-appraised sources. Heuristics are essentially rules of thumb that helps us make a decision in real environments, and are often as accurate as a more complicated decision process. A distinguishing feature is that the Levels cover the entire range of clinical questions, in the order (from top row to bottom row) that the clinician requires. While most ranking schemes consider strength of evidence for therapeutic effects and harms, the OCEBM system allows clinicians and patients to appraise evidence for prevalence, accuracy of diagnostic tests, prognosis, therapeutic effects, rare harms, common harms, and usefulness of (early) screening. Pre-appraised sources such as the Trip Database (1), (2), or REHAB+ (3) are useful because people who have the time and expertise to conduct systematic reviews of all the evidence design them. At the same time, clinicians, patients, and others may wish to keep some of the power over critical appraisal in their own hands. History Evidence ranking schemes have been used, and criticised, for decades (4-7), and each scheme is geared to answer different questions(8). Early evidence hierarchies(5, 6, 9) were introduced primarily to help clinicians and other researchers appraise the quality of evidence for therapeutic effects, while more recent attempts to assign levels to evidence have been designed to help systematic reviewers(8), or guideline developers(10). -
The Hierarchy of Evidence Pyramid
The Hierarchy of Evidence Pyramid Available from: https://www.researchgate.net/figure/Hierarchy-of-evidence-pyramid-The-pyramidal-shape-qualitatively-integrates-the- amount-of_fig1_311504831 [accessed 12 Dec, 2019] Available from: https://journals.lww.com/clinorthop/Fulltext/2003/08000/Hierarchy_of_Evidence__From_Case_Reports_to.4.aspx [accessed 14 March 2020] CLINICAL ORTHOPAEDICS AND RELATED RESEARCH Number 413, pp. 19–24 © 2003 Lippincott Williams & Wilkins, Inc. Hierarchy of Evidence: From Case Reports to Randomized Controlled Trials Brian Brighton, MD*; Mohit Bhandari, MD, MSc**; Downloaded from https://journals.lww.com/clinorthop by BhDMf5ePHKav1zEoum1tQfN4a+kJLhEZgbsIHo4XMi0hCywCX1AWnYQp/IlQrHD3oaxD/v Paul Tornetta, III, MD†; and David T. Felson, MD* In the hierarchy of research designs, the results This hierarchy has not been supported in two re- of randomized controlled trials are considered cent publications in the New England Journal of the highest level of evidence. Randomization is Medicine which identified nonsignificant differ- the only method for controlling for known and ences in results between randomized, controlled unknown prognostic factors between two com- trials, and observational studies. The current au- parison groups. Lack of randomization predis- thors provide an approach to organizing pub- poses a study to potentially important imbal- lished research on the basis of study design, a hi- ances in baseline characteristics between two erarchy of evidence, a set of principles and tools study groups. There is a hierarchy of evidence, that help clinicians distinguish ignorance of evi- with randomized controlled trials at the top, con- dence from real scientific uncertainty, distin- trolled observational studies in the middle, and guish evidence from unsubstantiated opinions, uncontrolled studies and opinion at the bottom. -
Technical Writing
Technical Writing Engineers and scientists perform many complex and intricate tasks using the world's most sophisticated equipment. However, their performance as engineers and scientists is almost always related to their use of one of the oldest tools - the pen. In academia, the saying "publish or perish" often describes the process of acquiring tenure as well as credibility. In industry, both large and small organizations communicate everything through memos, reports, and short presentations. Product development decisions are often made by a committee of people far removed from the actual technology. The saying "he who has the most convincing viewgraphs and reports, wins..." can sometimes apply to industry. Therefore, it should be clear that an ability to concisely and efficiently prepare technical reports, research papers, and or viewgraph presentations can have a profound positive impact on an individual's career. Consider the following statement by anonymous Fortune 500 corporate vice president: "... in any large organization, the person who decides whether you get a promotion, or who determines the size of a pay raise, does not know you personally. The only thing they have to go on is what other people write about you and what you write about you ..." It can be seen that if one should write a lot of material to get ahead in one's career, it makes sense to write as objectively and concisely as possible. Objective writing is essential because good technical writing should not be seen as erroneous after new discoveries are made. A good technical report should present a clear milestone of what was done and understood at the time of the writing. -
Concepts and Definitions for Identifying R&D
Frascati Manual 2015 Guidelines for Collecting and Reporting Data on Research and Experimental Development © OECD 2015 Chapter 2 Concepts and definitions for identifying R&D This chapter provides the definition of research and experimental development (R&D) and of its components, basic research, applied research and experimental development. These definitions are essentially unchanged from those in previous editions of the manual. Where there are differences, they reflect changes in culture and the use of language. To provide guidance on what is and what is not an R&D activity, five criteria are provided which require the activity to be novel, creative, uncertain in it outcome, systematic and transferable and/or reproducible. Since the last edition, the treatment of R&D expenditure in the System of National Accounts (SNA) has changed from an expense to a capital investment. As a result, the language of this manual, and of the SNA, is closer and there are additional requirements for measurements of financial flows. While the manual has always applied to all scientific disciplines, there is more emphasis on the social sciences, humanities and the arts, in addition to the natural sciences and engineering. Measuring R&D activities through surveys, administrative data, or interviews raises questions about boundaries and what is and what is not included and this chapter provides examples to help answer those questions. The manual is used to interpret R&D data as part of policy development and evaluation, but the focus of this chapter is on definitions for measurement purposes. 43 I-2. CONCEPTS AND DEFINITIONS FOR IDENTIFYING R&D 2.1. -
Foundations of Nursing Science 9781284041347 CH01.Indd Page 2 10/23/13 10:44 AM Ff-446 /207/JB00090/Work/Indd
9781284041347_CH01.indd Page 1 10/23/13 10:44 AM ff-446 /207/JB00090/work/indd © Jones & Bartlett Learning, LLC. NOT FOR SALE OR DISTRIBUTION PART 1 Foundations of Nursing Science 9781284041347_CH01.indd Page 2 10/23/13 10:44 AM ff-446 /207/JB00090/work/indd © Jones & Bartlett Learning, LLC. NOT FOR SALE OR DISTRIBUTION 9781284041347_CH01.indd Page 3 10/23/13 10:44 AM ff-446 /207/JB00090/work/indd © Jones & Bartlett Learning, LLC. NOT FOR SALE OR DISTRIBUTION CHAPTER Philosophy of Science: An Introduction 1 E. Carol Polifroni Introduction A philosophy of science is a perspective—a lens, a way one views the world, and, in the case of advanced practice nurses, the viewpoint the nurse acts from in every encounter with a patient, family, or group. A person’s philosophy of science cre- ates the frame on a picture—a message that becomes a paradigm and a point of reference. Each individual’s philosophy of science will permit some things to be seen and cause others to be blocked. It allows people to be open to some thoughts and potentially keeps them closed to others. A philosophy will deem some ideas correct, others inconsistent, and some simply wrong. While philosophy of sci- ence is not meant to be viewed as a black or white proposition, it does provide perspectives that include some ideas and thoughts and, therefore, it must neces- sarily exclude others. The important key is to ensure that the ideas and thoughts within a given philosophy remain consistent with one another, rather than being in opposition.