Bibliographic record
Abstract
Assertions of knowledge—that is, assertions which constitute ascriptions of knowledge—have served both as a central source of data in the epistemological theorizing and as an important target of such theorizing themselves. This chapter considers three issues concerning knowledge ascriptions that have figured prominently in recent discussion: their use as evidence for specific epistemological claims and theories, the pragmatics and psychology of knowledge ascriptions, and the social role(s) that the relevant assertions might play. There are important interactions among these topics; and they, and positions taken in response to them, interact as well with central questions about both assertion and linguistic communication generally. One central point on which epistemologists divide, however, is over how closely tied various features of our knowledge-attributing behavior are to semantics, or to (presumed) facts about assertion per se.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.010 |
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".