Bibliographic record
Abstract
Throughout his work on the rationality of epistemic dependence, John Hardwig makes the striking observation that he believes many things for which he possesses no evidence (1985, 335; 1991, 693; 1994, 83). While he could imagine collecting for himself the relevant evidence for some of his beliefs, the vastness of the world and constraints of time and individual intellect thwart his ability to gather for himself the evidence for all his beliefs. So for many things he believes what others tell him, as we all do. Epistemic dependence is the responsible choice, he argues, because he can be reasonably sure that those on whom he depends know more about the subject than he does. Epistemic dependence on experts is a smarter bet than epistemic autonomy: after all, Hardwig reasons, “if I were to pursue epistemic autonomy across the board, I would succeed in holding relatively uninformed, unreliable, crude, untested, and therefore irrational beliefs” (1985, 340) [...] In this paper I argue against what I call Hardwig’s no-evidence thesis: that knowledge and belief based on testimony are knowledge and belief for which the knower possesses no evidence. Against the no-evidence thesis, I propose we recognize that layperson B’s good reason to believe that expert A has good reason to believe proposition p constitutes evidence for B for p. I argue that the reasons Hardwig gives for the no-evidence thesis are inconclusive at best; at worst the no-evidence thesis coupled with his recognition of expert interdependence exposes him to recent criticisms by Stella Gaon and Stephen Norris. By rejecting the no-evidence thesis, we can recognize with Hardwig the importance of expert epistemic interdependence while avoiding the paradoxical implications of his position.
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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 | 0.020 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.041 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".