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Summary In Philosophy of Science, 'scientific practice' refers to activities whose aim is the achievement of scientific goals. More specifically, the category of scientific practice covers everything scientists do when they engage in the production of scientific knowledge. These activities include discovering, experimenting, measuring, modeling, observing, predicting, simulating, and so on, as well as using instruments in the pursuit of scientific goals. In recent years, there has been a shift in Philosophy of Science from an emphasis on scientific theories to an emphasis on actual scientific practices (see, for example, the mission statement of the Society for Philosophy of Science in Practice at http://www.philosophy-science-practice.org/en/).
Key works Some key works include Kuhn 1962, Hacking 1983, Longino 1990, Solomon 2001, Wylie 2002, Baird 2002, Chang 2004, and Douglas 2009.
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  1. Russel L. Ackoff (1954). Book Review:An Introduction to Scientific Research E. Bright Wilson, Jr. [REVIEW] Philosophy of Science 21 (4):354-.
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  2. Diederik Aerts, Jan Broekaert & Liane Gabora (1999). Editorial: Formal and Informal Representations of Science. [REVIEW] Foundations of Science 4 (1):1-2.
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  3. Joseph Agassi (1984). III. The Cheapening of Science∗. Inquiry 27 (1-4):166-172.
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  4. Joseph Agassi (1980). Between Science and Technology. Philosophy of Science 47 (1):82-99.
    Basic research or fundamental research is distinct from both pure and applied research, in that it is pure research with expected useful results. The existence of basic or fundamental research is problematic, at least for both inductivists and instrumentalists, but also for Popper. Assuming scientific research to be the search for explanatory conjectures and for refutations, and assuming technology to be the search of conjectures and some corroborations, we can easily place basic or fundamental research between science and technology as (...)
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  5. Joseph Agassi (1968). No More Discovery in Physics? [REVIEW] Synthese 18 (1):103-108.
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  6. Evandro Agazzi (2012). Rethinking Philosophy of Science Today. Journal of Philosophical Research 37 (Supplement):85-101.
    Modern philosophy of science was, initially, an epistemology of science based on the logical analysis of the language of science. It was superseded by a “sociological epistemology,” according to which the acceptance of scientific statements and theories depends on conditioningscoming from the social context and powers, and this view has fueled anti-scientific attitudes.This happened because the sociological turn still expressed an epistemology of science. Science, however, is not only a system of knowledge, but also a complex human activity. Hence, ethical, (...)
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  7. Atocha Aliseda (2004). Logics in Scientific Discovery. Foundations of Science 9 (3):339-363.
    In this paper I argue for a place for logic inscientific methodology, at the same level asthat of computational and historicalapproaches. While it is well known that a awhole generation of philosophers dismissedLogical Positivism (not just for the logicthough), there are at least two reasons toreconsider logical approaches in the philosophyof science. On the one hand, the presentsituation in logical research has gone farbeyond the formal developments that deductivelogic reached last century, and new researchincludes the formalization of several othertypes of (...)
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  8. Norman H. Anderson (1970). Functional Measurement and Psychophysical Judgment. Psychological Review 77 (3):153-170.
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  9. Philip W. Anderson (1997). Is Measurement Itself an Emergent Property? Complexity 3 (1):14-16.
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  10. Peter R. Anstey (forthcoming). Locke on Measurement. Studies in History and Philosophy of Science Part A.
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  11. Jochen Apel, Monika Dullstein & Pawel Radchenko (2009). Data-Phenomena-Theories: What’s the Notion of a Scientific Phenomenon Good For? Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 40 (1):125-128.
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  12. Eckhart Arnold, Tools for Evaluating the Consequences of Prior Knowledge, but No Experiments. On the Role of Computer Simulations in Science.
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  13. Eckhart Arnold (2014). What’s Wrong with Social Simulations? The Monist 97 (3):359-377.
    This paper tries to answer the question why the epistemic value of so many social simulations is questionable. I consider the epistemic value of a social simulation as questionable if it contributes neither directly nor indirectly to the understanding of empirical reality. To examine this question, two classical social simulations are analyzed with respect to their possible epistemic justification: Schelling’s neighborhood segregation model and Axelrod’s reiterated Prisoner’s Dilemma simulations of the evolution of cooperation . It is argued that Schelling’s simulation (...)
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  14. Eckhart Arnold, The Dark Side of the Force: When Computer Simulations Lead Us Astray and ``Model Think'' Narrows Our Imagination --- Pre Conference Draft for the Models and Simulation Conference, Paris, June 12-14 ---. [REVIEW]
    This paper is intended as a critical examination of the question of when the use of computer simulations is beneficial to scientific explanations. This objective is pursued in two steps: First, I try to establish clear criteria that simulations must meet in order to be explanatory. Basically, a simulation has explanatory power only if it includes all causally relevant factors of a given empirical configuration and if the simulation delivers stable results within the measurement inaccuracies of the input parameters. If (...)
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  15. Eckhart Arnold (2006). The Dark Side of the Force. When Computer Simulations Lead Us Astray and Model Think Narrows Our Imagination. In Homepage Eckhart Arnold. Preprint.
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  16. A. C. Arokiam, A. V. Barashev, D. J. Bacon & Y. N. Osetsky (2007). Atomic-Scale Computer Simulation Study of the Interaction of Cu-Rich Precipitates with Irradiation-Produced Defects in Α-Fe. Philosophical Magazine 87 (6):925-943.
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  17. Naomi Aronson (1986). The Discovery of Resistance Historical Accounts and Scientific Careers. Isis 77 (4):630-646.
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  18. Jacques Arsac & Académie D'éducation Et D'études Sociales (2000). Au Risque de la Science les Conséquences Éducatives Et Sociales du Développement Scientifique Et Technique. Annales 1999-2000.
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  19. Christopher J. Austin (2016). Causality: An Empirically Informed Plea for Pluralism. Metascience 25 (2):293-296.
    Phyllis Illari & Federica Russo: Causality: Philosophical Theory Meets Scientific Practice. Oxford: Oxford University Press, 2014, 310pp, £29.99 HB.
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  20. D. J. Bacon, Y. N. Osetsky & Z. Rong (2006). Computer Simulation of Reactions Between an Edge Dislocation and Glissile Self-Interstitial Clusters in Iron. Philosophical Magazine 86 (25-26):3921-3936.
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  21. Lawrence Badash (2000). Science and McCarthyism. Minerva 38 (1):53-80.
    Students of the `long' McCarthy period in the United States – fromthe late 1940s through the 1950s – have paid inadequate attentionto the effects of this oppressive time upon science. Visa andpassport denials, loyalty oaths, security investigations, andother problems placed in the paths of scientists no doubthindered science. But they also increased the political maturityof its practitioners, a fact of which recent events make usparticularly aware.
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  22. D. Baird (2004). The End of Pure Science: Science Policy From Bayh-Dole to the NNI. In Baird D. (ed.), Discovering the Nanoscale. Ios. pp. 217.
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  23. Davis Baird & Alfred Nordmann (1994). Facts-Well-Put. British Journal for the Philosophy of Science 45 (1):37-77.
    In this paper we elucidate a particular type of instrument. Striking-phenomenon instruments assume their striking profile against the shifting backdrop of theoretical uncertainties. While technologically stable, the phenomena produced by these instruments are linguistically fuzzy, subject to a variety of conceptual representations. But in virtue of their technological stability alone, they can provide a foundation for further technological as well as conceptual development. Sometimes, as in the case of the pulse glass, the phenomenon is taken to confirm conflicting theoretical views; (...)
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  24. Mark Balaguer, Elaine Landry, Sorin Bangu & Christopher Pincock (2013). Structures, Fictions, and the Explanatory Epistemology of Mathematics in Science. Metascience 22 (2):247-273.
  25. Aristides Baltas (2004). On the Grammatical Aspects of Radical Scientific Discovery. Philosophia Scientae 8:169-201.
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  26. W. Balzer & E.-W. Haendler (1989). Ordinary Least Squares as a Method of Measurement. Erkenntnis 30 (1-2):129 - 146.
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  27. Anouk Barberousse, Henri Galinon & Marion Vorms, Collaborative Computer Simulations in Climate Science.
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  28. Eduardo Salles de Oliveira Barra (2013). A Ciência E o Projeto Crítico Kantiano. Scientiae Studia 11 (4):937-962.
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  29. Ditta Bartels (1985). Science in Society. Metascience 3:3.
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  30. Ruth Barton (1998). Just Before Nature: The Purposes of Science and the Purposes of Popularization in Some English Popular Science Journals of the 1860s. Annals of Science 55 (1):1-33.
    Summary Popular science journalism flourished in the 1860s in England, with many new journals being projected. The time was ripe, Victorian men of science believed, for an ?organ of science? to provide a means of communication between specialties, and between men of science and the public. New formats were tried as new purposes emerged. Popular science journalism became less recreational and educational. Editorial commentary and reviewing the progress of science became more important. The analysis here emphasizes those aspects of popular (...)
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  31. Diderik Batens, Jean Paul van Bendegem & International Union of the History and Philosophy of Science (1988). Theory and Experiment Recent Insights and New Perspectives on Their Relation.
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  32. Claus Beisbart (2012). How Can Computer Simulations Produce New Knowledge? European Journal for Philosophy of Science 2 (3):395-434.
    It is often claimed that scientists can obtain new knowledge about nature by running computer simulations. How is this possible? I answer this question by arguing that computer simulations are arguments. This view parallels Norton’s argument view about thought experiments. I show that computer simulations can be reconstructed as arguments that fully capture the epistemic power of the simulations. Assuming the extended mind hypothesis, I furthermore argue that running the computer simulation is to execute the reconstructing argument. I discuss some (...)
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  33. Mara Beller (2003). Inevitability, Inseparability and Gedanken Measurement. In A. Ashtekar (ed.), Revisiting the Foundations of Relativistic Physics. pp. 439--450.
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  34. Andrew Belsey (1987). Objectivity, Science and Society: Interpreting Nature and Society in the Age of the Crisis of Science. Philosophical Books 28 (3):188-189.
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  35. M. Ben-Chaim (2001). The Scientific Discovery of 'Natural Capital': The Production of Catalytic Antibodies. Studies in History and Philosophy of Science Part C 32 (3):413-433.
    Modern science has undoubtedly become one the principal engines of economic growth, even though the epistemological status of scientific knowledge has been continuously contested. Leaving the philosophical problem of knowledge aside, this paper examines how scientific discovery contributes to the production of wealth. The analysis focuses on a recent achievement at the crossroads of chemistry, immunology and biotechnology: antibody catalysis. For this purpose, we develop a model of entrepreneurial work to explain how the discovery of natural products and processes generates (...)
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  36. A. Cornelius Benjamin (1934). The Mystery of Scientific Discovery. Philosophy of Science 1 (2):224-236.
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  37. G. Bergmann & K. W. Spence (1944). The Logic of Psychophysical Measurement. Psychological Review 51 (1):1-24.
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  38. Jesús P. Zamora Bonilla (1999). Representaciones En la Ciencia: De la Invariancia Estructural a la Significatividad Pragmática. Theoria 14 (2):380-382.
  39. Mieke Boon (2015). The Scientific Use of Technological Instruments. In Sven Ove Hansson (ed.), The Role of Technology in Science: Philosophical Perspectives. Springer Verlag.
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  40. Mieke Boon (2008). Diagrammatic Models in the Engineering Sciences. Foundations of Science 13 (2):127-142.
    This paper is concerned with scientific reasoning in the engineering sciences. Engineering sciences aim at explaining, predicting and describing physical phenomena occurring in technological devices. The focus of this paper is on mathematical description. These mathematical descriptions are important to computer-aided engineering or design programs (CAE and CAD). The first part of this paper explains why a traditional view, according to which scientific laws explain and predict phenomena and processes, is problematic. In the second part, the reasons of these methodological (...)
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  41. Mieke Boon (2006). How Science is Applied in Technology. International Studies in the Philosophy of Science 20 (1):27 – 47.
    Unlike basic sciences, scientific research in advanced technologies aims to explain, predict, and (mathematically) describe not phenomena in nature, but phenomena in technological artefacts, thereby producing knowledge that is utilized in technological design. This article first explains why the covering-law view of applying science is inadequate for characterizing this research practice. Instead, the covering-law approach and causal explanation are integrated in this practice. Ludwig Prandtl's approach to concrete fluid flows is used as an example of scientific research in the engineering (...)
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  42. Mieke Boon (2004). Technological Instruments in Scientific Experimentation. International Studies in the Philosophy of Science 18 (2 & 3):221 – 230.
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  43. Georgina Born & Andrew Barry (2013). To Public Experiment. In Andrew Barry & Georgina Born (eds.), Interdisciplinarity: Reconfigurations of the Social and Natural Sciences. Routledge. pp. 247.
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  44. Emanuela Bosco, Mary V. Bastawrous, Ron H. J. Peerlings, Johan P. M. Hoefnagels & Marc G. D. Geers (2015). Bridging Network Properties to the Effective Hygro-Expansivity of Paper: Experiments and Modelling. Philosophical Magazine 95 (28-30):3385-3401.
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  45. Marcel Boumans, Giora Hon & Arthur Petersen (eds.) (forthcoming). Error and Uncertainty in Scientific Practice. Pickering & Chatto.
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  46. David Braddon‐Mitchell (1991). Nature's Capacities and Their Measurement. Philosophical Books 32 (4):201-209.
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  47. C. G. Bradford (1915). An Experiment in Association. Psychological Review 22 (4):279-288.
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  48. Walter Brinke, David Squire & John Bigelow, Similarity: Measurement, Ordering and Betweenness.
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  49. Harcourt Brown, Karl Wolfgagng Deutsch & American Council of Learned Societies Devoted to Humanistic Studies (1958). Science and the Creative Spirit Essays on Humanistic Aspects of Science. University of Toronto Press.
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  50. Anna Brozek & Jacek Jadacki (2012). Thought Experiments in Science. Filozofia Nauki 20 (1).
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