Le cognitivisme moral de Habermas fait-il face au problème de Frege-Geach?1
Bibliographic record
Abstract
L’article cherche à fournir une défense de la théorie discursive de la morale de Habermas contre une critique importante formulée récemment par J. G. Finlayson, lequel soutient que Habermas rejetterait ce qu’il appelle le « cognitivisme métaéthique » et qu’un tel rejet le confronterait au problème de Frege-Geach. L’article démontre en détail que cette critique est non fondée. Il montre de plus que la seule forme de cognitivisme rejetée par Habermas est le descriptivisme moral en ce que cette approche serait contre-intuitive eu égard à l’usage normal de nos expressions morales. L’article cherche finalement à répondre à certaines objections majeures que les philosophes descriptivistes pourraient soulever à l’endroit de la théorie habermassienne de la morale, en particulier contre sa thèse de l’analogie entre vérité propositionnelle et justesse normative.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".