Bibliographic record
Abstract
Selon certains philosophes, le principe « devoir-implique-pouvoir » (DIP) exprime une vérité analytique et permet d’inférer des énoncés normatifs sur la base de prémisses purement descriptives. Le principe DIP offrirait ainsi un contre-exemple à la fameuse loi de Hume. Le problème est que DIP permet uniquement de construire des raisonnements dont les conclusions sont des négations de normes . Or, selon certains auteurs, les négations de normes n’expriment pas de véritables jugements moraux (Nelson, 1995). En ce sens, selon eux, DIP ne permet pas de tirer des conclusions normatives sur la base de prémisses uniquement descriptives. Dans cet article, nous soutenons au contraire que les négations de normes sont de véritables normes et que DIP permet bien de déduire une conclusion normative à partir de prémisses purement descriptives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".