Hostile Work Environment Rules: Comparative Analysis of U.S. and Canadian Laws
Bibliographic record
Abstract
The MeToo Movement shocked the world and encouraged multiple victims of workplace harassment to disclose the severity of the problem. A few years have come since the onset of this notorious process, and it is reasonable to investigate if modern countries have adequate legislation to address the selected problem. Therefore, the legislation of the United States and Canada is the object of this research. The comparative research methodology is used to identify the applicable laws, compare them, and comment on the notable findings. This approach is practical and helpful since it highlights efficient strategies that other countries can use to succeed in the selected area. The research has identified that the two countries differ in the sources of law, enforcement, remedies, and definition conditions. Canada impresses with a more tailored approach to satisfy its local needs, but the two countries should regularly update and improve their legislation to address new harassment manifestations and challenges.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".