Pourquoi légiférer l’éthique ? Pour apaiser le public ou pour soutenir l’exercice d’une charge publique ?
Bibliographic record
Abstract
Tous s’entendent pour affirmer que les titulaires de charges publiques doivent se conformer à de hautes normes d’éthique et de déontologie dans l’exécution de leur mandat. Peut-on y parvenir sans que des prescriptions à ce sujet soient inscrites dans la législation ? La réponse suggérée est non, car seules des lois permettront au public de bien connaître les règles de conduite auxquelles doivent se conformer les titulaires de charges publiques. Pour ces derniers, la législation permet de fixer avec clarté et continuité les paramètres déontologiques à l’intérieur desquels ils doivent exercer leur mandat. Somme toute, il n’y a pas en ce domaine d’autre solution valable que la législation.
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 | 0.036 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".