La loi sur le devoir de vigilance des sociétés multinationales : parcours d’une loi improbable
Bibliographic record
Abstract
Cet article propose une analyse des obstacles rencontrés par les défenseurs de la proposition de loi sur le devoir de vigilance (ONG et syndicats) en étudiant les efforts de plaidoyer et de lobbying déployés par ces derniers afin de réussir à faire déposer, examiner puis adopter cette loi dont le Gouvernement ne voulait pas initialement. À l’aune de ce cas, le concept d’entrepreneur institutionnel est discuté puis relativisé par l’utilisation de la notion de fortuna , développée par Machiavel, afin de décrire le passage « improbable » de cette loi. Les résultats tendent ainsi à relativiser l’importance que des acteurs singuliers, y compris collectifs, peuvent avoir dans l’explication du changement institutionnel, au profit d’une analyse multi-niveaux du changement (micro, méso, macro).
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".