Bibliographic record
Abstract
Il y a lieu de se réjouir de la montée en puissance des préoccupations sociétales et environnementales à laquelle nous sommes en train d’assister, notamment au sein des entreprises. Mais cette montée en puissance soulève de multiples défis... En particulier, comment faire pour que les sujets environnementaux et sociétaux, malgré leur complexité intrinsèque et les passions qu’ils suscitent, soient traités avec le maximum de pertinence ? Cette question se trouve bien entendu posée au sein des entreprises. Mais elle l’est également au sein de l’État, qui a une responsabilité en la matière (une responsabilité directe lorsqu’il s’agit de lui-même ou d’entreprises publiques, et indirecte lorsqu’il s’agit d’entreprises privées). Nous essayerons d’apporter ici quelques éléments de réponse qui mettent en avant ce que peuvent apporter la recherche, des expertises pointues et multidisciplinaires, des processus de gouvernance complets et cohérents au sein des entreprises, la mutualisation des expériences, la formation, l’information et le débat.
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 | 0.023 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".