Introduction - La vie privée du salarié dans la jurisprudence comparée
Bibliographic record
Abstract
Le présent article ouvre le dossier en comparant les jurisprudences françaises, américaines, chinoises et canadiennes sur deux thèmes: les liaisons entre salariés et l’utilisation par ces derniers des réseaux sociaux. Le législateur est parfois intervenu en la matière mais incontestablement c’est le juge qui reste maître du l’application du régime juridique, ne serait-ce qu’en raison de la subjectivité du thème. Qu’est-ce que la vie privée ? Dans quelle mesure doit-elle être protégée par rapport aux attentes légitimes d’un employeur ? L’analyse de la jurisprudence met en lumière de grandes divergences suivant les pays. Cependant il est intéressant de constater que certaines solutions sont comparables et reposent parfois sur le bons sens plutôt que sur des constructions jurisprudentielles : c’est le cas des sanctions qui frappent les salariés trop indiscrets qui utilisent les réseaux sociaux.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".