Bibliographic record
Abstract
G. A. Cohen, in his Rescuing Justice and Equality, argues that fundamental moral principles do not rest on factual grounds. I contest that and argue instead that all fundamental moral principles (indeed, all moral principles) are fact-sensitive. They are the most deeply embedded principles in an interdependent web of beliefs—beliefs which include factual beliefs. Indeed, all functioning moral beliefs, moral principles and moral practices are in such interdependent webs. There are no fundamental moral principles which are fact-insensitive. What is fundamental are the most deeply embedded moral principles in interdependent webs of belief and practice. If you will, forms of life. G.A. Cohen, dans son livre Rescuing Justice and Equality, maintient que les principes moraux fondamentaux ne reposent pas sur des bases factuelles. Je conteste cette idée et argumente que tous les principes moraux fondamentaux (en fait tous les principes moraux) sont sensibles aux faits. Ce sont les principes les plus profondément enracinés dans des faisceaux de croyances interdépendantes – croyances qui comprennent des croyances factuelles. Toutes les croyances et pratiques, tous les principes moraux fonctionnels sont pris dans ce genre de faisceaux interdépendants. Il n’y a pas de principes moraux fondamentaux qui soient insensibles aux faits. Plus ils sont fondamentaux, plus les principes moraux sont profondément enracinés dans des faisceaux interdépendants de croyances et de pratiques. Ainsi en est-il des façons de vivre.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.036 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".