Bibliographic record
Abstract
Abstract Some recent epistemologists propose that certainty is the norm of action and assertion. This proposal is subject to skeptical worries. If, as is usually supposed, certainty is very hard to come by, legitimate action and assertion will be rare. To remedy this, some have conjoined their certainty-norms with a context-sensitive semantics for ‘certainty’. For a proposition to be certain for you, you only need to be able to exclude relevant alternatives. I argue that, depending on what makes an alternative relevant, this kind of view is disingenuous. In particular, if an alternative can be made relevant by being relevant to rational action, it allows an escape from the skeptical consequences only by licensing David Lewis-style utterances of the form, “You know that p only if there is no probability, no matter how small, that not-p—Psst!—Unless that probability is really small.” While there are legitimate ways to exclude some possibilities from relevance, it is disingenuous, I argue, to exclude possibilities from relevance on the basis of the very characteristic—low but nonzero probability—that is claimed to be incompatible with certainty.
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 | 0.022 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.052 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".