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Understanding Scientific Study via Process Modeling

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Abstract

This paper argues that scientific studies distinguish themselves from other studies by a combination of their processes, their (knowledge) elements and the roles of these elements. This is supported by constructing a process model. An illustrative example based on Newtonian mechanics shows how scientific knowledge is structured according to the process model. To distinguish scientific studies from research and scientific research, two additional process models are built for such processes. We apply these process models: (1) to argue that scientific progress should emphasize both the process of change and the content of change; (2) to chart the major stages of scientific study development; and (3) to define “science”.

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References

  • Achinstein P. (1965) Theoretical models. The British Journal of Philosophy of Science 16(62): 102–120

    Article  Google Scholar 

  • Alexander P. (1958) Theory-construction and theory-testing. The British Journal of Philosophy of Science 9(33): 29–38

    Article  Google Scholar 

  • Aliseda A. (2004) Logics in scientific discovery. Foundations of Science 9(3): 339–363

    Article  Google Scholar 

  • Bailer-Jones D. M. (1999) Creative strategies employed in modeling: A case study. Foundations of Science 4(4): 375–388

    Article  Google Scholar 

  • Bailer-Jones D. (2003) When scientific models represent. International Studies in the Philosophy of Science 17(1): 59–74

    Article  Google Scholar 

  • Bartholomew D. J., Knott M. (1999) Latent variable models and factor analysis. Arnold, London

    Google Scholar 

  • Baumann P. (2004) Theory choice and the intransitivity of is a better theory than. Philosophy of Science 72(1): 231–240

    Article  Google Scholar 

  • Berners-Lee T., Hall W., Hendler J., Shadbolt N., Weitzner D. J. (2006) Creating a science of the web. Science 313(5788): 769–771

    Article  Google Scholar 

  • Beth E. W. (1951) Fundamental features of contemporary theory of science. The British Journal of Philosophy of Science 1(4): 291–302

    Article  Google Scholar 

  • Beveridge W. I. B. (1957) The art of scientific study. Norton & Company, New York

    Google Scholar 

  • Białkowski G. (1998) Is physics an universal science?. Foundations of Science 1(1): 9–21

    Article  Google Scholar 

  • Bhaskar R., Simon H. A. (1977) Problem solving in semantically rich domains: An example from engineering thermodynamics. Cognitive Science 1(2): 193–215

    Article  Google Scholar 

  • Bod R. (2006) Towards a general model of applying science. International Studies in the Philosophy of Science 20(1): 5–25

    Article  Google Scholar 

  • Bod R., Boon M., Boumans M. (2006) Introduction to the symposium ‘applying science’. International Studies in the Philosophy of Science 20(1): 1–3

    Article  Google Scholar 

  • Boon M. (2003) Technological instruments in scientific experimentation. International Studies in the Philosophy of Science 18(2–3): 221–230

    Google Scholar 

  • Boon M. (2006) How science is applied in technology. International Studies in the Philosophy of Science 20(1): 27–47

    Article  Google Scholar 

  • Broadbent D. E. (1977) Levels, hierarchies, and the locus of control. Quarterly Journal of Experimental Psychology 29: 181–201

    Article  Google Scholar 

  • Brodaric B., Gahegan M. (2006) Representing geoscientific knowledge in cyberinfrastructure: Challenges, approaches and implementations. In: Sinha A. K. (eds) Geoinformatics: Data to knowledge. Geological Society of America Inc, Colorado, USA, pp 1–20

    Chapter  Google Scholar 

  • Chalmers A. F. (1999) What is this thing called science?. Open University Press, Buckingham

    Google Scholar 

  • Cleland C. E. (2001) Historical science, experimental science and the scientific method. Geology 29(11): 987–990

    Article  Google Scholar 

  • Damper, R. I. (2006). Thought experiments can be harmful. The Pantaneto Forum 22.

  • Davy K. (2003) Is mathematical rigor necessary in physics?. The British Journal of Philosophy of Scence 53(3): 439–463

    Article  Google Scholar 

  • De Roure D., Jennings N. R., Shadbolt N. R. (2003) The semantic grid: A future e-science infrastructure. In: Berman F., Fox G., Hey T. (eds) Grid computing—making the global infrastructure a reality. Wiley, England, pp 437–470

    Google Scholar 

  • Dörner D. (1975) Wie Menschen eine Welt verbessern wollten (How people wanted to improve the world). Bild der Wissenschaft 12: 48–53

    Google Scholar 

  • Einstein A. (1905) Zur Electrodynamik bewegter Körper (On the electrodynamics of moving bodies). Annalen der Physik 17: 891–921

    Article  Google Scholar 

  • Franklin A. D. (1981) What makes a ‘good’ experiment?. The British Journal of Philosophy of Science 32(4): 367–374

    Article  Google Scholar 

  • Franklin A. D., Anderson M., Brock D., Coleman S., Downing J., Gruvander A., Lilly J., Neal J., Peterson D., Price M., Rice R., Smith L., Speirer S., Toering D. (1989) Can a theory-laden observation test the theory. The British Journal of Philosophy of Science 40(2): 229–231

    Article  Google Scholar 

  • Feyerabend P. K. (1975) Against method: Outline of an anarchistic theory of knowledge. New Left Books, London

    Google Scholar 

  • Gauch H. C. (2003) Scientific method in practice. Cambridge University Press, Cambridge

    Google Scholar 

  • Giere R. N. (2004) How models are used to represent reality. Philosophy of Science 71(5): 742–752

    Article  Google Scholar 

  • Hansson S. O. (2006) Falsificationism falsified. Foundations of Science 11(3): 275–286

    Article  Google Scholar 

  • Hansson S. O. (2007) Values in pure and applied science. Foundations of Science 12(3): 257–268

    Article  Google Scholar 

  • Hars A. (2001) Designing scientific knowledge infrastructures: The contribution of epistemology. Information Systems Frontiers 3(1): 63–71

    Article  Google Scholar 

  • Hartmann S. (1996) The world as a process: Simulations in the natural and social sciences. In: Hegselmann R., Mueller U., Troitzsch K. G. (eds) Modeling and simulation in the social sciences from the philosophy of science point of view. Kluwer, Dordrecht, pp 77–100

    Google Scholar 

  • Hennig, C. (2010). Mathematical models and reality: A constructivist perspective. Foundations of Science (To appear).

  • Humphreys P. (1995) Computational empiricism. Foundations of Science 1(1): 119–130

    Google Scholar 

  • Kingston, J. (2002). Merging top level ontologies for scientific knowledge management. In Proceedings of the AAAI workshop on ontologies and the semantic web, Edmonton, Canada.

  • Korshland D. E. Jr. (2007) The cha-cha-cha theory of scientific discovery. Science 317(5839): 761–762

    Article  Google Scholar 

  • Kosso P. (2007) Scientific understanding. Foundations of Science 12(2): 173–188

    Article  Google Scholar 

  • Kruse M. (1997) Variation and the accuracy of predictions. The British Journal of Philosophy of Science 48(2): 181–193

    Article  Google Scholar 

  • Kuhn T. S. (1962) The Structure of Scientific Revolutions. University of Chicago Press, Chicago

    Google Scholar 

  • Kuhn T. S. (1977) Objectivity, value judgment and theory choice. In: Kuhn T. S. (eds) The essential tension: Selected studies in scientific tradition and change. University of Chicago Press, Chicago, pp 320–339

    Google Scholar 

  • Lakatos I. (1977). The methodology of scientific research programmes. In: Worrall J., Currie G. (eds). Cambridge: Cambridge University Press

  • Laudan L. (1987) Progress or rationality? The prospects for normative naturalism. American Philosophical Quarterly 24: 19–31

    Google Scholar 

  • Liu C. (1997) Models and theories I: The semantic view revisited. International Studies in the Philosophy of Science 11(2): 147–164

    Article  Google Scholar 

  • Liu C. (1998) Models and theories II: Issues and applications. International Studies in the Philosophy of Science 12(2): 111–128

    Article  Google Scholar 

  • Liu C. (1999) Approximation, idealization, and laws of nature. Synthese 118(2): 229–256

    Article  Google Scholar 

  • Liu C. (2004) Laws and models in a theory of idealization. Syntheses 138(3): 363–385

    Article  Google Scholar 

  • Ludäscher M., Lin K., Bowers S., Jaeger-Frank E., Brodaric B., Baru C. (2006) Managing scientific data: From data integration to scientific workflows. In: Sinha A. K. (eds) Geoinformatics: Data to knowledge. Geological Society of America Inc, Colorado, USA, pp 109–130

    Chapter  Google Scholar 

  • Magnani L. (1999) Withdrawing unfalsifiable hypothesis. Foundations of Science 4(2): 257–268

    Article  Google Scholar 

  • Marquis J.-P. (1991) Approximations and truth space. Journal of Philosophical Logic 20(4): 375–401

    Article  Google Scholar 

  • Mattingly J. (2005) The structure of scientific theory change: Models versus privileged formulations. Philosophy of Science 72(2): 365–389

    Article  Google Scholar 

  • McMullin E. (1985) Galilean idealization. Studies in the History and Philosophy of Science 16: 247–273

    Article  Google Scholar 

  • Miscevic N. (2001) Science, commonsense and philosophy: A defense of continuity (a critique of network apriorism). International Studies in the Philosophy of Science 15(1): 19–31

    Article  Google Scholar 

  • Moor J. H. (1978) Three myths of computer science. The British Journal for the Philosophy of Science 29(3): 213–222

    Article  Google Scholar 

  • Morrison M. (2006) Applying science and applied science: What’s the difference?. International Studies in the Philosophy of Science 20(1): 81–91

    Article  Google Scholar 

  • Morton A. (1993) Mathematical models: Questions of trustworthiness. The British Journal of Philosophy of Science 44(4): 659–674

    Article  Google Scholar 

  • Nagel T. (1974) What is it like to be a bat?. Philosophical Review 83(4): 435–450

    Article  Google Scholar 

  • Niiniluoto I. (1987) Truthlikeness. Reidel, Dordrecht

    Google Scholar 

  • Nowak L. (1972) Laws of science, theories, measurement. Philosophy of Science 39(4): 533–548

    Article  Google Scholar 

  • Nugavey R. (1985) A study of theory unification. The British Journal for the Philosophy of Science 36(2): 159–173

    Article  Google Scholar 

  • Oelkers J. (1998) Empirical research in progressive education. International Journal of Educational Research 27(8): 715–722

    Article  Google Scholar 

  • Pierce C. S. (1878) Deduction, induction and abduction. Popular Science Monthly 13: 470–782

    Google Scholar 

  • Popper K. R. (1972) Objective knowledge. Oxford University Press, Oxford

    Google Scholar 

  • Popper K. R. (1959) The logic of scientific discovery. Hutchinson, London

    Google Scholar 

  • Portides D. P. (2005) A theory of scientific model construction: The conceptual process of abstraction and concretization. Foundations of Science 10(1): 67–88

    Article  Google Scholar 

  • Psillos S. (2000) Agnostic empiricism versus scientific realism: Belief in truth matters. International Studies in the Philosophy of Science 14(1): 57–75

    Article  Google Scholar 

  • Rainville S., Thompson J. K., Myers E. G., Brown J. M., Dewey M. S., Kessler E. G. Jr., Deslattes R. D., Börner H. G., Jentschel M., Mutti P., Pritchard D. E. (2005) A direct test of Emc 2. Nature 438(22): 1096–1097

    Article  Google Scholar 

  • Saracevic P. B., Kantor T. (1997) Studying the value of library and information services. Part II. Methodology and taxonomy. Journal of the American Society for Information Science and Technology 48(6): 543–563

    Article  Google Scholar 

  • Shadbolt N. R., Gibbins N., Glaser H., Harris S., Schraefel M. C. (2004) Walking through CS AKTive space: A demonstration of an integrated semantic web application. Journal of Web Semantics 1(4): 415–420

    Google Scholar 

  • Silberschatz A., Korth H. F., Sudarshan S. (2005) Database system concepts. McGraw Hill, New York

    Google Scholar 

  • Simon H. (1977) Models of discovery: And other topics in the methods of science. D. Reidel Pub Co, Dordrecht, Holland; Boston

    Google Scholar 

  • Sintonen M. (2004) Reasoning to hypotheses: Where do questions come from?. Foundations of Science 9(3): 249–266

    Article  Google Scholar 

  • Struan J. (2001) Limits of problem solving in science. International Studies in the Philosophy of Science 15(3): 231–242

    Article  Google Scholar 

  • Suárez M. (2003) Scientific representation: Against similarity and isomorphism. International Studies in the Philosophy of Science 17(3): 225–244

    Article  Google Scholar 

  • Suppes P. (1960) A comparison of meaning and use of models in mathematics and empirical sciences. Synthese 12(2-3): 287–301

    Article  Google Scholar 

  • Suppes P. (1995) Principles that transcend experience: Kant’s antinomies revisited (Transzendentale prinzipien: Eine neubetrachtung der Kantschen antinomien). Metaphysik 11: 43–54

    Google Scholar 

  • Suppes P. (2007) Statistical concepts in philosophy of science. Synthese 154(3): 485–496

    Article  Google Scholar 

  • Swanson J. W. (1967) On models. The British Journal of Philosophy of Science 17(4): 297–311

    Article  Google Scholar 

  • Thagard P. (1993) Computational philosophy of science. MIT Press, Reading, MA

    Google Scholar 

  • von Bertalanffy L. (1950) An outline of general system theory. The British Journal for the Philosophy of Science 1(2): 134–165

    Article  Google Scholar 

  • Weston T. (1987) Approximate truth. Journal of Philosophical Logic 16(2): 203–227

    Article  Google Scholar 

  • Weinburg G. M. (2001) An introduction to general systems thinking. Dorset House, New York

    Google Scholar 

  • Wiezenbaum J. (1966) ELIZA—a computer program for the study of natural language communication between man and machine. Communications of the ACM 9(1): 36–45

    Article  Google Scholar 

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Correspondence to Robert W. P. Luk.

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Luk, R.W.P. Understanding Scientific Study via Process Modeling. Found Sci 15, 49–78 (2010). https://doi.org/10.1007/s10699-009-9168-9

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