Philosophical Studies

, Volume 134, Issue 2, pp 111–129

How to Link Assertion and Knowledge Without Going Contextualist: A Reply to Derose’s "Assertion, Knowledge, and Context"

Article

Abstract

Keith DeRose has recently argued that the contextual variability of appropriate assertion, together with the knowledge account of assertion, yields a direct argument that ’knows’ is semantically contextsensitive. The argument fails because of an equivocation on the notion of warranted assertability. Once the equivocation is removed, it can be seen that the invariantist can retain the knowledge account of assertion and explain the contextual variability of appropriate assertion by appealing to Williamson’s suggestion that practical and conversational considerations can influence the extent to which adherence to the constitutive norm of assertion matters.

Keywords

assertion contextualism DeRose epistemology Williamsonm 

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References

  1. Author (year) Publication 1.Google Scholar
  2. Blackson Thomas A. (2004): ’An Invalid Argument for Contextualism’. Philosophy and Phenomenological Research 68(2): 344–345CrossRefGoogle Scholar
  3. DeRose Keith (1992): ’Contextualism and Knowledge Attributions’. Philosophy and Phenomenological Research 52: 913–929CrossRefGoogle Scholar
  4. DeRose Keith (2004): ’The Problem with Subject-Sensitive Invariantism’. Philosophy and Phenomenological Research 68(2): 346–350CrossRefGoogle Scholar
  5. DeRose Keith (2002): ’Assertion, Knowledge, and Context’. The Philosophical Review 111(2): 167–204CrossRefGoogle Scholar
  6. Rysiew Patrick (2001): ’The Context-Sensitivity of Knowledge Attributions’. Noûs 35(4): 477–514Google Scholar
  7. Williamson Timothy (2001): Knowledge and Its Limits, Oxford University Press.Google Scholar

Copyright information

© Springer Science+Business Media, Inc. 2007

Authors and Affiliations

  1. 1.Department of philosophyIndiana UniversityBloomingtonUSA

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