HOW CAN WE USE THE DISTINCTION BETWEEN DISCOVERY AND JUSTIFICATION? ON THE WEAKNESSES OF THE STRONG PROGRAMME IN THE SOCIOLOGY OF SCIENCE

  • THOMAS STURM
  • GERD GIGERENZER
Part of the Archimedes book series (ARIM, volume 14)

Abstract

Ever since Kuhn’s The Structure of Scientific Revolutions (Kuhn 1962, 1970), many philosophers, historians, and sociologists of science have attacked the distinction between discovery and justification (the DJ distinction). It has been argued that the distinction cannot be drawn precisely; that it cannot be drawn prior to the actual analysis of scientific knowledge; that it is useless for the analysis of scientific knowledge; and that perhaps there is no such distinction at all. Other critics, instead of trying to blur or to reject the distinction, claim that we need an even more fine-grained distinction. Avariety of concepts such as generation, invention, prior assessment, evaluation, test, proof, and so on, is needed, depending on the different kinds of questions we can raise concerning scientific research and its results (e.g., Nickels 1980b, pp. 18–22; Hoyningen-Huene 1987, pp. 507–509).

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WORKS CITED

  1. Aaronson, D., Grupsmith, E. and Aaronson, M. (1976), “The Impact of Computers on Cognitive Psychology,” Behavioral Research Methods & Instrumentation 8: 129–138.Google Scholar
  2. Arabatzis, T. (1994), “Rational versus Sociological Reductionism: Imre Lakatos and the Edinburgh School,” in K. Gavroglu et al. (eds.), Trends in the Historiography of Science (Dordrecht: Kluwer), pp. 177–192.Google Scholar
  3. Barnes, B. (1972), “Sociological Explanation and Natural Science: A Kuhnian Reappraisal,” Archives Européens de Sociologie 13: 373–393.Google Scholar
  4. Barnes, B., Bloor, D. and Henry, J. (1996), Scientific Knowledge: A Sociological Analysis (Chicago: University of Chicago Press).Google Scholar
  5. Bloor, D. (1976; 1991), Knowledge and Social Imagery (London: Routledge & Kegan Paul).Google Scholar
  6. Bloor, D. (1983), Wittgenstein: A Social Theory of Knowledge (London: MacMillan).Google Scholar
  7. Bloor, D. (1984), “The Strenghts of the Strong Programme,” in J. R. Brown (ed.), Scientific Rationality: The Sociological Turn (Dordrecht: Reidel), pp. 75–94.Google Scholar
  8. Brandtstadter, J. and Sturm, T. (2004), “Aprioritat, Erfahrung und das Projekt der Psychologie,” Zeitschrift für Sozialpsychologie 35: 15–32.Google Scholar
  9. Brunswik, E. (1943), “Organismic Achievement and Environmental Probability,” Psychological Review 50: 255–272.Google Scholar
  10. Castellan, N. J. (1981), “On-line Computers in Psychology: The last 10 years, the next 10 years—The Challenge and the Promise,” Behavioral Research Methods & Instrumentation 13: pp. 91–96.Google Scholar
  11. Danziger, K. (1990), Constructing the Subject: Historical Origins of Psychological Research (Cambridge: Cambridge University Press).Google Scholar
  12. Davidson, D. (1963), “Actions, Reasons and Causes,” in D. Davidson, Essays on Actions and Events (Oxford: Oxford University Press), pp. 3–20.Google Scholar
  13. Draaisma, D. (2000), Metaphors of Memory (Cambridge: Cambridge University Press).Google Scholar
  14. Edgington, E. E. (1974), “A New Tabulation of Statistical Procedures Used in APA Journals,” American Psychologist 29: 25–26.Google Scholar
  15. Fine, A. (1996), “Science Made Up: Constructivist Sociology of Scientific Knowledge,” in P. Galison and D. Stump (eds.), The Disunity of Science (Stanford: Stanford University Press), pp. 231–254.Google Scholar
  16. Friedman, M. (1998), “On the Sociology of Scientific Knowledge and Its Philosophical Agenda,” Studies in History and Philosophy of Science 29: 239–271.CrossRefGoogle Scholar
  17. Gigerenzer, G. (1992), “Discovery in Cognitive Psychology: New Tools Inspire New Theories,” Science in Context 5: 329–350.Google Scholar
  18. Gigerenzer, G. (2003), Adaptive Thinking: Rationality in the Real World (New York: Oxford University Press).Google Scholar
  19. Gigerenzer, G., and Goldstein, D. G. (1996). “Mind as Computer: Birth of a metaphor,” Creativity Research Journal 9: 131–144.CrossRefGoogle Scholar
  20. Gigerenzer, G. and Murray, D. J. (1987), Cognition as Intuitive Statistics (Hillsdale: Erlbaum).Google Scholar
  21. Gigerenzer, G. and Sturm, T. (forthcoming), “Tools = Theories = Data? On Some Circular Dynamics in Cognitive Science,” in M. Ash and T. Sturm (eds.), Psychology's Territories (Mahwah, NJ: Erlbaum).Google Scholar
  22. Gigerenzer, G., Swijtink, Z., Porter, T., Daston, L., Beatty, J., and Krüger, L. (1989), The Empire of Chance: How Probability Changed Science and Everyday Life (Cambridge: Cambridge University Press).Google Scholar
  23. Hacking, I. (1983), Representing and Intervening (Cambridge: Cambridge University Press).Google Scholar
  24. Hacking, I. (1999), The Social Construction of What? (Cambridge, MA: Harvard University Press).Google Scholar
  25. Haddock, A (2004), “Rethinking the ‘Strong Programme’ in the Sociology of Knowledge,” Studies in the History and Philosophy of Science 35: 19–40.CrossRefGoogle Scholar
  26. Heidelberger, M. (2003), “Theory-ladenness and Scientific Instruments in Experimentation,” in H. Radder (ed.), The Philosophy of Scientific Experimentation (Pittsburgh: Pittsburgh University Press), 138–151.Google Scholar
  27. Hesse, M. (1985), “The Strong Thesis in the Sociology of Science,” in M. Hesse, Revolutions and Reconstructions in the Philosophy of Science (Bloomington: Indiana University Press), pp. 29–60.Google Scholar
  28. Hoyningen-Huene, P. (1987), “Context of Discovery and Context of Justification,” Studies in the History and Philosophy of Science 18: 501–515.CrossRefGoogle Scholar
  29. Kant, I. (1781/1787; 1997), Critique of Pure Reason, ed. and transl.by P. Guyer (Cambridge: Cambridge University Press).Google Scholar
  30. Kantorovich, A. (1993), Scientific Discovery—Logic and Tinkering (New York: State University of New York Press).Google Scholar
  31. Kelley, H. H. (1967), “Attribution Theory in Social Psychology,” in D. Levine (ed.), Nebraska Symposium on Motivation, Vol. 15 (Lincoln: University of Nebraska Press).Google Scholar
  32. Kelley, H. H. and Michaela, I. L. (1980), “Attribution Theory and Research,” Annual Review of Psychology 31, 457–501.CrossRefGoogle Scholar
  33. Kitcher, P. (1993), The Advancement of Science (Oxford: Oxford University Press).Google Scholar
  34. Knorr-Cetina, K. (1981), The Manufacture of Knowledge (Oxford: Pergamon Press).Google Scholar
  35. Knorr-Cetina, K. and Mulkay, M. (eds.) (1982), Science Observed (London: Sage).Google Scholar
  36. Krüger, L. (1974), “Wissenschaftliche Revolution und Kontinuität der Erfahrung,” Neue Hefte für Philosophie 6/7: 1–26.Google Scholar
  37. Krüger, L. (1983), “Empirismus oder Realismus-eine Alternative in der Wissenschaftstheorie?,” in G. Frey and J. Zegler (eds.), Der Mensch und die Wissenschaften vom Menschen (Innsbruck.) Vol. II, pp. 569–587.Google Scholar
  38. Kuhn, T. S. (1962; 1970a), The Structure of Scientific Revolutions (Chicago: University of Chicago Press).Google Scholar
  39. Kuhn, T. S. (1970b), “Reflection on my Critics,” in I. Lakatos and A. Musgrave (eds.), Criticism and the Growth of Knowledge (Cambridge: Cambridge University Press), pp. 231–278.Google Scholar
  40. Kuhn, T. S. (1977), “Objectivity, Value Judgment, and Theory Choice,” in T. S. Kuhn, The Essential Tension (Chicago: University of Chicago Press), pp. 320–339.Google Scholar
  41. Latour, B. and Woolgar, S. (1979; 1986), Laboratory Life (Princeton: Princeton University Press).Google Scholar
  42. Langley, P., Simon, H. A., Bradshaw, G. L. and Zytkow, J. M. (1987), Scientific Discovery (Cambridge, MA: MIT Press).Google Scholar
  43. Laudan, L. (1984a), Science and Values (Berkeley: University of California Press).Google Scholar
  44. Laudan, L. (1984b), “The Pseudo-Science of Science?,” in J. R. Brown (ed.), Scientific Rationality: The Sociological Turn (Dordrecht: Reidel), pp. 41–74.Google Scholar
  45. Leary, D. E. (1987), “From Act Psychology to Probabilistic Functionalism: The Place of Egon Brunswik in the History of Psychology,” in M. G. Ash and W. R. Woodward (eds.), Psychology in Twentieth-Century Thought and Society (Cambridge: Cambridge University Press), pp. 115–142.Google Scholar
  46. Lenoir, T. (1986), “Models and Instruments in the Development of Electrophysiology, 1845–1912,” Historical Studies in the Physical Sciences 17: 1–54.Google Scholar
  47. McCorduck, P. (1979), Machines Who Think (San Francisco: Freeman & Co.).Google Scholar
  48. McMullin, E. (1984), “The Rational and the Social in the History of Science,” in J. R. Brown (ed.), Scientific Rationality: The Sociological Turn (Dordrecht: Reidel), pp. 127–163.Google Scholar
  49. Mele, A. and Rawlings, P. (eds.) (2004), The Oxford Handbook of Rationality (Oxford: Oxford University Press).Google Scholar
  50. Merton, R. K. (1973), The Sociology of Science (ed.) by Norman W. Storer (Chicago: University of Chicago Press).Google Scholar
  51. Michotte, A. (1963), The Perception of Causality (London: Methuen).Google Scholar
  52. Nelson, A. (1994), “How Could Scientific Facts Be Socially Constructed?,” Studies in the History and Philosophy of Science 25: 535–457.CrossRefGoogle Scholar
  53. Newell, A., Shaw, J. C. and Simon, H. A. (1958), “Elements of a Theory of Human Problem Solving,” Psychological Review 65: 151–166.Google Scholar
  54. Newell, A. and Simon. H. A. (1972), Human Problem Solving (Englewood Cliffs: Prentice-Hall).Google Scholar
  55. Nickles, T. (ed.) (1980a), Scientific Discovery, Logic, and Rationality (Dordrecht: Reidel).Google Scholar
  56. Nickles, T. (1980b), “Introductory Essay: Scientific Discovery and the Future of Philosophy,” in Nickles 1980a, pp. 1–59.Google Scholar
  57. Okasha, S. (2000), “The Underdetermination of Theory by Data and the ‘Strong Programme’ in the Sociology of Knowledge,” International Studies in the Philosophy of Science 14: 283–297.Google Scholar
  58. Papineau, D. (2003), “Comments on Gerd Gigerenzer,” in M. C. Galavotti (ed.), Observation and Experiment in the Natural and Social Sciences (Dordrecht: Kluwer), pp. 141–151.Google Scholar
  59. Piaget, J. (1930), The Child's Conception of Causality (London: Kegan Paul).Google Scholar
  60. Pickering, A. (ed.) (1992), Science as Culture and as Practice (Chicago: Chicago University Press).Google Scholar
  61. Putnam, H. (1960), “Minds and Machines,” in S. Hook(ed.), Dimensions of Mind (New York: New York University Press), pp. 138–164.Google Scholar
  62. Quine, W. V. O. (1978), “A Postscript on Metaphor,” in S. Sacks (ed.), On Metaphor (Chicago: University of Chicago Press), pp. 159–160.Google Scholar
  63. Reichenbach, H. (1938), Experience and Prediction (Chicago: University of Chicago Press), 1938.Google Scholar
  64. Schaffer, S. (1994), “Making Up Discovery,” in M. Boden (ed.), Dimensions of Creativity (Cambridge, MA: MIT Press), pp. 13–51.Google Scholar
  65. Siegel, H. (1980), “Justification, Discovery and the Naturalizing of Epistemology,” Philosophy of Science 47:297–321.CrossRefGoogle Scholar
  66. Simon, H. A. (1979), “Information Processing Models of Cognition.” Annual Review of Psychology 30: 363–96.CrossRefGoogle Scholar
  67. Smith, L. D. (1990), “Metaphors of Knowledge and Behavior in the Behaviorist Tradition,” in D. Leary (ed.), Metaphors in the History of Psychology (Cambridge: Cambridge University Press), 239–266.Google Scholar
  68. Sokal, A. and Bricmont, J. (1998), Fashionable Nonsense (New York: Picador).Google Scholar
  69. Sterling, T. D. (1959), “Publication Decisions and their Possible Effects on Inferences Drawn from Tests of Significance or vice versa,” Journal of the American Statistical Association 54: 30–34.Google Scholar
  70. Tanner, W. P., Jr. (1965), Statistical Decision Processes in Detection and Recognition (Technical Report) (Ann Arbor: University of Michigan, Sensory Intelligence Laboratory, Department of Psychology).Google Scholar

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© Springer 2006

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  • THOMAS STURM
  • GERD GIGERENZER

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