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  1.  34
    J. W. McAllister (1996). The Evidential Significance of Thought Experiment in Science. Studies in History and Philosophy of Science Part A 27 (2):233-250.
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  2.  3
    J. W. McAllister, Dilemmas in Science: What, Why, and How.
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  3.  12
    J. W. McAllister (2003). Algorithmic Randomness in Empirical Data. Studies in History and Philosophy of Science Part A 34 (3):633-646.
    According to a traditional view, scientific laws and theories constitute algorithmic compressions of empirical data sets collected from observations and measurements. This article defends the thesis that, to the contrary, empirical data sets are algorithmically incompressible. The reason is that individual data points are determined partly by perturbations, or causal factors that cannot be reduced to any pattern. If empirical data sets are incompressible, then they exhibit maximal algorithmic complexity, maximal entropy and zero redundancy. They are therefore maximally efficient carriers (...)
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  4.  17
    J. W. McAllister, Emotion, Rationality, and Decision Making in Science.
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  5.  13
    J. W. McAllister, Is Beauty a Sign of Truth in Scientific Theories?
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  6.  19
    J. W. McAllister, Mathematical Beauty and the Evolution of the Standards of Mathematical Proof.
  7.  7
    J. W. McAllister, The Virtual Laboratory: Thought Experiments in Seventeenth-Century Mechanics.
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  8. J. W. Mcallister (2000). Picturing Science, Producing Art: Edited by Caroline A. Jones and Peter Galison. British Journal of Aesthetics 40 (2):270-271.
     
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  9.  5
    J. W. McAllister, "Warrants for Truth" Review of J. Leplin, A Novel Defense of Scientific Realism (New York: Oxford University Press, 1997) and R. G. Newton, The Truth of Science: Physical Theories and Reality (Cambridge, Mass.: Harvard University Press, 1997). [REVIEW]
  10.  12
    J. W. McAllister, Dirac and the Aesthetic Evaluation of Theories.
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  11.  9
    J. W. McAllister, Relativism.
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  12.  11
    J. W. McAllister (2005). Algorithmic Compression of Empirical Data: Reply to Twardy, Gardner, and Dowe. Studies in History and Philosophy of Science Part A 36 (2):403-410.
    This discussion note responds to objections by Twardy, Gardner, and Dowe to my earlier claim that empirical data sets are algorithmically incompressible. Twardy, Gardner, and Dowe hold that many empirical data sets are compressible by Minimum Message Length technique and offer this as evidence that these data sets are algorithmically compressible. I reply that the compression achieved by Minimum Message Length technique is different from algorithmic compression. I conclude that Twardy, Gardner, and Dowe fail to establish that empirical data sets (...)
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  13.  9
    J. W. McAllister, Historical and Structural Approaches in the Natural and Human Sciences.
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  14.  11
    J. W. McAllister, C. A. Jones and P. Galison, Eds., Picturing Science, Producing Art (London: Routledge, 1998).
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  15.  9
    J. W. McAllister, A. Zee, Fearful Symmetry: The Search for Beauty in Modern Physics (Princeton, N.J.: Princeton University Press, 1999).
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  16.  3
    J. W. McAllister, The Integration of European Higher Education and the Nature of Philosophy.
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  17.  7
    J. W. McAllister (1998). Author's Response. Metascience 7 (1):112-116.
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  18.  2
    J. W. McAllister, The Formation of Styles: Science and the Applied Arts.
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  19.  4
    J. W. McAllister (1990). P. Redondi and PV Pillai, Eds., The History of Sciences: The French Debate (London: Sangam Books, 1989). International Studies in the Philosophy of Science 4 (2):221-223.
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  20.  1
    J. W. McAllister, Unification of Theories.
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  21.  1
    J. W. McAllister (2005). Editor's Report, 2004. International Studies in the Philosophy of Science 19 (2):101-103.
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  22. J. W. McAllister, Boekbespreking. R. A. Sorensen, Thought Experiments (New York: Oxford University Press, 1992).
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  23. J. W. McAllister, Editor's Report, 2003.
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