Bibliographic record
Abstract
L'auteure étudie en un premier temps les différents recours offerts aux victimes de harcèlement sexuel au Québec et les avantages qu’elles peuvent en tirer. Elle discute ensuite des poursuites en libelle diffamatoire intentées contre ces victimes qui sont parfois utilisées en guise de représailles aux plaintes de harcèlement sexuel. De son analyse découle une critique des mesures de redressement octroyées aux victimes de harcèlement sexuel. L'analyse jurisprudentielle démontre que les montants accordés en matière de dommages moraux ne sont pas toujours adéquats pour indemniser pleinement les victimes de harcèlement sexuel. Il y a lieu de croire que les tribunaux devraient préciser mieux les facteurs utilisés pour quantifier les dommages moraux. L'octroi de mesures de redressement «proactives » pourrait peut-être aider davantage à remédier au problème du harcèlement sexuel en milieu de travail.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".