Student-on-Student Harassment: A New Paradigm for Canadian Human Rights Legislation
Bibliographic record
Abstract
Canadian school boards have recently begun to find themselves in the position of respondent under human rights complaints filed for cases of student-on-student harassment. This has raised serious questions regarding the extent of school board liability for the acts of students. It is possible for the current human rights legislation in Nova Scotia, used as a model in this discussion, to have jurisdiction over peer harassment; however, the procedural and substantive obstacles that need to be overcome make it a far from satisfactory avenue for a victim to pursue. The recent United States Supreme Court case of Davis v. Monroe County Board of Education has established that it is possible to ground such a complaint under the relevant American federal human rights legislation. If the reasoning in that case lends any guidance as to how a similar issue may be resolved in Canada, it is that there is not much that separates a human rights complaint from a civil action in negligence or breach of fiduciary duty. The effectiveness of human rights regimes in protecting victims of student-on-student harassment needs to be assessed in light of the increasing frequency of such incidents.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.035 | 0.042 |
| Scholarly communication | 0.023 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".