Bibliographic record
Abstract
Assertions are subject to norms. Someone’s assertion might be poignant or clever or eloquent, and these can be reasons to make it; or it might be rude or distracting or racist, which might explain why it shouldn’t have been asserted. So much, so obvious. Many philosophers have suspected that there are also more distinctive senses in which assertions are subject to norms. For example, there is a large literature considering: (Knowledge Norm of Assertion) One must: assert p only if one knows p. If this norm is correct, it is tied more closely to assertion than any norms about eloquence or rudeness are. The reason one should not assert rudely is that in general, one should not do rude things. If one should not assert unknowningly, that is for a more specific reason; there is no general prohibition on doing things unknowingly (it is for example perfectly acceptable to ask or wonder whether p without knowing p).1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.089 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".