Bibliographic record
Abstract
Dans cet article, je m'intéresse à quelques problèmes liés à la justification de méta-normes telles que « devoir » implique « pouvoir ». Je soutiens, d'abord, que des méta-normes différentes sont justifiées par des principes différents. La méta-norme « devoir » implique « pouvoir » est justifiée par un principe d'humanité qui nous demande d'exclure les normes trop exigeantes. La métanorme selon laquelle ce serait une sottise d'ordonner à quelqu'un ce qu'il veut inévitablement de lui-même ( « le rasoir de Kant ») est justifiée par un principe de parcimonie qui nous demande d'exclure les normes trop peu exigeantes. Je soutiens ensuite que ces deux principes (humanité et parcimonie) peuvent entrer en conflit. Ce qui m'amène à la conclusion que toute théorie normative qui s'efforce de respecter ces deux principes risque d'être instable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".