Targeting Workplace Harassment in Quebec: On Exporting a New Legislative Agenda
Bibliographic record
Abstract
For over twenty-five years, Canadian law has prohibited sexual harassment and other forms of discriminatory harassment (meaning harassment that relates to the target's race, disability, sexual orientation, or other similar characteristic). Recent amendments to the Quebec Labour Standards Act aim to fill a gap in the law that currently provides a remedy (under human rights legislation) for discriminatory harassment, but not for harassment that is not obviously linked to the target's membership in a protected class such as that based on race, sex, religion, disability or sexual orientation. This paper takes a preliminary look at this new legislative initiative. It first outlines briefly the minimal legal protections that existed in Quebec and throughout Canada before the psychological harassment law was introduced. It then describes key features of the law before examining some elements of the Quebec social and legal context that shaped the law and that may render the agenda less "exportable" to other Canadian and American jurisdictions. Finally, the paper suggests that it is worth considering the relationship between status-blind harassment and discriminatory harassment, as well as the effect of our attempts to address discriminatory harassment in current economic and political contexts, in an effort to see both the possibilities and potential pitfalls of this new legislative agenda.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".