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John L. King [4]John Leslie King [2]
  1. John Leslie King (1983). Balance in an Unbalanced Environment. Acm Sigcas Computers and Society 13 (1):10-16.
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  2. John L. King (1979). Bivalence and the Sorites Paradox. American Philosophical Quarterly 16 (1):17 - 25.
    Putative resolutions of the sorites paradox in which the major premise is declared false or illegitimate, Including max black's treatment in terms of the alleged illegitimacy of vague attributions to borderline cases, Are rejected on semantical grounds. The resort to a non-Bivalent logic of representational "accuracy" with a continuum of accuracy values is shown to resolve the paradox, And the identification of accuracy values as truth values is defended as compatible with the central insight of the correspondence theory of truth (...)
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  3. John L. King (1979). Coextensiveness and Lawlikeness. Erkenntnis 14 (3):359 - 363.
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  4. John L. King (1978). Chandler on Contingent Identity. Analysis 38 (3):135 - 136.
    In his article "rigid designation" ("journal of philosophy", Volume lxxii, Pages 363-9) hugh s chandler presents an alleged counterexample to the principles that proper names are rigid designators and that identity statements using proper names as designators are non-Contingent. In the present paper this counterexample is shown to rest on a tacit assumption which the principles' proponents need not accept. Chandler's example is redescribed in a way which is both plausible and compatible with the two principles.
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  5. Kenneth L. Kraemer, John Leslie King & Kent Colton (1977). Towards an Agenda for EFT Research. Acm Sigcas Computers and Society 8 (2):3-11.
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  6. John L. King (1976). Statistical Relevance and Explanatory Classification. Philosophical Studies 30 (5):313 - 321.
    Numerous philosophers, among them Carl G. Hempel and Wesley C. Salmon, have attempted to explicate the notion of explanatory relevance in terms of the statistical relevance of various properties of an individual to the explanandum property itself (or what is here called narrow statistical relevance). This approach seems plausible if one assumes that to explain an occurrence is to show that it was to be expected or to exhibit its degree of expectability and the factors which influence its expectability. But (...)
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