Qualification juridique du harcèlement moral en France : étude empirique des arrêts des cours d’appel de la région AquitaineÉtude empirique des arrêts des cours d’appel de la région Aquitaine
Bibliographic record
Abstract
L’analyse du contentieux du harcelement moral pose des questions de recherche auxquelles la methode juridique ne permet pas toujours de repondre. Les trop rares etudes contentieuses portent davantage sur la mobilisation des regles que sur les facteurs de la decision du juge. Les methodes de l’epidemiologie qui permettent une description detaillee et une analyse des elements associes aux decisions du juge peuvent offrir un point de vue original sur le contentieux. A partir de la base JURICA, nous avons decrit les arrets des cours d’appel d’Aquitaine (France) de l’annee 2011 traitant de situations de harcelement moral au travail et mis en evidence les elements associes a sa reconnaissance par le juge. Parmi les 136 arrets identifies comme traitant specifiquement du harcelement moral, dans 38 cas la qualification a ete retenue par le juge. Cette qualification etait plus frequente quand la victime etait une femme, lorsqu’une consultation avec un medecin avait mene a un arret maladie, lorsqu’une discrimination ou des nuisances liees au management avaient ete evoquees.
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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 | 0.026 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".