Bibliographic record
Abstract
ABSTRACT: Among recent criticisms of impartial moral theories, especially in consequentialist and deontological forms, Bernard Williams’ integrity objection is perhaps the most tantalizing. This objection is a complaint—at once both general and deep—that impartial moral theories are systematically incapable of finding room for integrity in human life and character. Kantians have made forceful responses to this integrity objection and have moved on. Consequentialists have found the objection more trying. I offer reasons to think that consequentialists too can safely move on. These reasons suggest the relationship between integrity and impartiality is less antagonistic than often supposed. RÉSUMÉ : Parmi les récentes critiques des théories morales impartiales, notamment les critiques conséquentialiste et déontologique, l’objection d’intégrité de Bernard Williams est possiblement la plus attrayante. Cette objection—à la fois générale et profonde—reproche aux théories morales impartiales d’être incapables de retrouver l’intégrité dans la vie et le caractère humains. Les kantiens ont répondu vigoureusement à cette objection, puis sont passés à autre chose. Les conséquentialistes ont trouvé l’objection plus éprouvante. Je soutiens que les conséquentialistes peuvent, eux aussi, passer à autre chose, en suggérant que le rapport entre l’intégrité et l’impartialité est moins antagonique que supposé.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.042 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".