Un code d'éthique, oui, mais comment?
Bibliographic record
Abstract
Résumé Le code d’éthique est l’instrument que les entreprises emploient le plus fréquemment pour développer une culture éthique. Toutefois, son adoption ne garantit ni l’amélioration de la culture organisationnelle, ni la conformité des comportements. Le problème provient souvent des conditions de mise au point et de gestion du code d’éthique. Cet article traite des tendances et des qualités attendues au regard du contenu des codes d’éthique. Il présente aussi une synthèse des étapes clés et des conditions de succès du développement, de l’implantation, de la gestion et de l’institutionnalisation d’une culture prônant véritablement l’éthique au sein des organisations. Le processus de gestion en trois étapes guide pas à pas les organisations dans leur démarche d’élaboration d’un code d’éthique.
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 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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".