Gender Implications of Wrongful Dismissal Judgments in Canada, 1994–2002*
Bibliographic record
Abstract
l'étude sur laquelle cet article se fonde explore les aboutissements des demandes d'carindemnités pour congédiement injustifié déposées par des hommes et des femmes contre leur ancien employeur. Elle révèle l'existence au sein du système juridique d'carun préjugé en faveur des hommes même si un traitement égal des deux sexes devant la loi est devenu un principe constitutionnel il y a 20 ans. l'analyse suggère que trois facteurs primaires, soit l'âge de l'employé(e), son ancienneté et le poste occupé au moment du congédiement, sont utilisés dans la détermination des jugements en dommages‐intérêts, et que les cours tendent à accorder de plus importantes indemnités aux hommes. The study on which this paper is based explored the outcomes of wrongful dismissal claims brought by men and women against their former employers. It revealed that a bias favouring men exists within the legal system, even though equal treatment of men and women under the law became a constitutional principle twenty years ago. Analysis suggests that three primary factors–the age of the employee, his or her job tenure, and the occupation held at the time of dismissal–are used to determine damage awards, and that courts tend to award the highest levels of compensation to men.
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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.020 | 0.004 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".