Bibliographic record
Abstract
Abstract Epistemically circular arguments have been receiving quite a bit of attention in the literature for the past decade or so. Often the goal is to determine whether reliabilists (or other foundationalists) are committed to the legitimacy of epistemically circular arguments. It is often assumed that epistemic circularity is objectionable, though sometimes reliabilists accept that their position entails the legitimacy of some epistemically circular arguments, and then go on to affirm that such arguments really are good ones. My goal in this paper is to argue against the legitimacy of epistemically circular arguments. My strategy is to give an argument against the legitimacy of epistemically circular arguments, which rests on a principle of basis-relative safety, and then to argue that reliabilists do not have the resources to resist the argument. I argue that even if the premises of an epistemically circular argument enjoy reliabilist justification, the argument does not transmit that justification to its conclusion. The main goal of my argument is to show that epistemic circularity is always a bad thing, but it also has the positive consequence that reliabilists are freed from an awkward commitment to the legitimacy of some intuitively bad arguments.
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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 | 0.027 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.041 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.010 |
| 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".