Pourquoi la conciliation volontaire n'est-elle pas plus efficace que la conciliation obligatoire ? L'adaptation inattendue des parties
Bibliographic record
Abstract
Les auteurs tentent, à partir des résultats d'une recherche présentés dans un article récent de la revue, d'expliquer pourquoi la conciliation volontaire n'est pas plus efficace que la conciliation obligatoire. La comparaison des objectifs, des comportements et des tactiques adoptés par les parties dans chacun des régimes fait ressortir des conclusions inattendues. Parmi celles-ci, les deux plus surprenantes sont les suivantes. Tout d'abord le changement de régime légal exerce une influence sur le comportement des parties non pas durant le processus mais au moment de son enclenchement. Ensuite il a engendré certains effets contre-productifs sur l'efficacité du processus par rapport au but visé par le législateur.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".