Bibliographic record
Abstract
In his comments, Garrett presents a fair summary of the project of the book: briefly, to understand Hume's account of reason in light of the non-formal accounts developed by, inter alia, Descartes and Locke. He also lays out, more clearly than I could do myself and with the succinct perspicuousness that anyone familiar with Garrett's work will immediately recognize, eight substantial, interpretative theses that I put forward and defend, and pleasingly announces his agreement with all of them. He does, alas, identify four rather important areas of disagreement, and I would like to say a little in this section about the first three. Garrett's first critical point concerns a very tendentious and difficult point in Hume scholarship: just what are Hume's views about body, and how are they constrained by his rigorous methodology?1 In my book, I made the strong claim that
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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 | 0.019 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.046 | 0.071 |
| Insufficient payload (model declined to judge) | 0.013 | 0.012 |
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".