Bibliographic record
Abstract
We argue, contrary to epistemological orthodoxy, that knowledge is not purely epistemic—that knowledge is not simply a matter of truth‐related factors (evidence, reliability, etc.). We do this by arguing for a pragmatic condition on knowledge, KA: if a subject knows that p, then she is rational to act as if p. KA, together with fallibilism, entails that knowledge is not purely epistemic. We support KA by appealing to the role of knowledge‐citations in defending and criticizing actions, and by giving a principled argument for KA, based on the inference rule KB: if a subject knows that A is the best thing she can do, she is rational to do A. In the second half of the paper, we consider and reject the two most promising objections to our case for KA, one based on the Gricean notion of conversational implicature and the other based on a contextualist maneuver.
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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 | 0.050 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.111 |
| Scholarly communication | 0.015 | 0.034 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.013 | 0.018 |
| 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".