Bibliographic record
Abstract
According to time-slice epistemology, what attitudes you should have at a time supervenes on features of you—like your evidence or mental states—at that time. Evidentialism is commonly assumed to be an instance of time-slice epistemology. I argue that if an influential argument against time-slice epistemology—that responding to evidence takes time—is sound, evidentialism is not an instance of time-slice epistemology. Therefore, evidentialism survives that argument even if time-slice epistemology doesn’t. Evidentialists must say that your evidence at a time is what can serve as a proper basis of your attitudes at that time. Therefore, if it takes time to properly base attitudes on your evidence, your evidence must predate your attitudes, in which case evidentialism is not an instance of time-slice epistemology. This doesn’t violate evidentialism’s internalist spirit, because if proper basing takes time, internalism itself is committed to the denial of time-slice epistemology. Contra some arguments from Brian Hedden and Andrew Moon, neither internalism nor evidentialism entails time-slice epistemology. If time-slice epistemology is false because proper basing takes time, then anything that internalists or evidentialists say justifies your attitudes must predate those attitudes.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| 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".