Les déviances dans les services de placement : responsabilités, sanctions et moyens de prévention
Bibliographic record
Abstract
Résumé Que se passe-t-il lorsque l’entreprise ou l’un de ses représentants dérogent aux obligations de loyauté, de transparence et de diligence qui leur incombent? Qui peut être tenu pour responsable : l’entreprise, le représentant ou les deux? Dans l’hypothèse où ces personnes sont tenues pour responsables, quelles sanctions peuvent-elles encourir? À la lumière d’exemples tirés de la jurisprudence, cet article explicite les différentes hypothèses où l’entreprise de services de placement et ses représentants peuvent engager leur responsabilité civile, administrative, disciplinaire, pénale et criminelle. L’article examine ensuite les divers types de sanctions auxquelles ces personnes s’exposent. Il fournit enfin des conseils aux employeurs en vue de prévenir ou de minimiser les manquements professionnels et, par la même occasion, le risque de poursuites et d’atteinte à leur réputation et à celle de leur personnel. Fonctions : GRH, comptabilité, finance, management, droit Secteurs : financier, valeurs mobilières, bancaire
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".