Bibliographic record
Abstract
This book focuses on recent debates about perceptual warrant. Against the ‘liberal’ views of Jim Pryor (an undefeated perceptual belief that P is justified iff one's experience represents that P (21)) and the ‘conservative’ views of Crispin Wright (an undefeated perceptual belief that P is justified iff one's experience represents that P, and ‘it is warrantedly assumed that there is an external world’ (29–30)), Coliva defends a ‘moderatism,’ according to which an undefeated perceptual belief that P is justified iff one's experience represents that P, and ‘it is assumed [unwarrantedly] that there is an external world’ (34). This ‘hinge epistemology’ turns on Wittgenstein's insight that empirical beliefs rest on assumptions that are themselves unfounded (1972: §253), and which serve as the ‘hinges’ (§§341 and 343) of our epistemic practices. Coliva sacrifices (125–126) Wittgenstein's distinctive treatment of ‘hinges’ as ‘norm[s] of description’ (1972: §167), but how different is her treatment of certain unfounded assumptions as constitutive of extended epistemic rationality? Her internalist, individualist starting point sits uneasily with Wittgenstein's pluralism (see Williams 1996), much as her endorsement of ‘an anti-realist conception of truth’ (37, 149) sits uneasily with Wittgenstein's deflationary sympathies. However, the idea that unfounded assumptions may still be ‘epistemically rational’ (129) deserves serious consideration from epistemologists.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".