Why Hume’s Censure of the Monkish Virtues Is Not Question-Begging
Bibliographic record
Abstract
Some consider Hume’s denunciation of what he calls the “monkish virtues” an unwarranted attack, redolent of an anticlerical bias. Hume rejects these virtues as antithetical to his own conception of happiness, so the complaint goes, without considering the possibility that when judged from the monkish point of view, they are both useful and agreeable. Only prejudice could explain such blatant question-begging. We argue, to the contrary, that when one reads Hume’s critique in light of his views on natural religion, it becomes apparent that the monkish, as Hume understands them, cultivate their virtues for ends other than happiness but unwisely. If Hume is right, the monkish virtues are worse than useless for monkish purposes, making them vices rather than virtues.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| 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".