Bibliographic record
Abstract
This paper examines the kind of epistemic circularity which, according to Ernest Sosa, is unavoidably entailed whenever one has what he calls ‘reflective’ knowledge (that is, knowledge that p such that the knower reflectively endorses the reliability of the epistemic sources by which she came to her belief that p). I begin by describing the relevant kind of circularity and its role in Sosa's epistemology, en route presenting and resisting Sosa's arguments that this kind of circularity is not vicious. Then I consider the somewhat complex relationship between Sosa's views on epistemic circularity and his response to the Problem of Easy Knowledge, arguing that (on one interpretation of Sosa, at least) a complete solution to that problem cannot be extracted from Sosa's work unless the aforementioned epistemic circularity can be proved non-vicious.
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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 | 0.025 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.073 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".