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Rejecting the New Statistical Solution to the Generality Problem

Published online by Cambridge University Press:  18 June 2019

Jeffrey Tolly Open the ORCID record for Jeffrey Tolly [Opens in a new window]
Jeffrey Tolly*
Affiliation:
University of Indianapolis, Indianapolis, Indiana, USA
*
*Corresponding author. Email: tollyj@uindy.edu
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Abstract

The generality problem is one of the most pressing challenges for process reliabilism about justification. Thus far, one of the more promising responses is James Beebe's tri-level statistical solution. Despite the initial plausibility of Beebe's approach, the tri-level statistical solution has been shown to generate implausible justification verdicts on a variety of cases. Recently, Samuel Kampa has offered a new statistical solution to the generality problem, which he argues can overcome the challenges that undermined Beebe's original statistical solution. However, there's good reason to believe that Kampa is mistaken. In this paper, I show that Kampa's new statistical solution faces problems that are no less serious than the original objections to Beebe's solution. Depending on how we interpret Kampa's proposal, the new statistical solution either types belief-forming processes far too narrowly, or the new statistical solution fails to clarify the epistemic implications of reliabilism altogether. Either way, the new statistical solution fails to make substantive progress towards solving the generality problem.

Keywords

Generality problemprocess reliabilismjustificationknowledge

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Type
Article
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Episteme , Volume 18 , Issue 2 , June 2021 , pp. 298 - 312
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Copyright © Cambridge University Press 2019

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