Bibliographic record
Abstract
Abstract We regularly make graded normative judgements in the epistemic domain. Recent work in the literature examines degrees of justification, degrees of rationality, and degrees of assertability. This paper addresses a different dimension of the gradeability of epistemic normativity, one that has been given little attention. How should we understand degrees of epistemic criticizability? In virtue of what sorts of factors can one epistemic failing be worse than another? The paper develops a dual-factor view of degrees of epistemic criticizability. According to the view, degrees of epistemic criticizability are (i) an inverse function of degrees of doxastic justification and (ii) a function of degrees of agent culpability. The paper develops an account of each factor, and explains how they should be weighted. The paper also addresses the importance of modelling degrees of epistemic criticizability in a broader context. I focus on the role that such a model can play in the ethics of epistemic criticism.
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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 | 0.019 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".