Choice, But No Choice: Adjudicating Human Rights Claims in Unionized Workplaces in Canada
Bibliographic record
Abstract
This paper explores the question of the proper forum for adjudicating workplace human rights claims for unionized employees in Canada. The author argues that the Supreme Court of Canada has directed a hybrid jurisdictional model, in which arbitrators have exclusive jurisdiction over some but not all such claims. Lower courts and tribunals have largely disobeyed this direction, rejecting the hybrid model in favour of unrestricted concurrency between statutory human rights tribunals and arbitration. A concurrency model theoretically permits unionized employees to choose where to take their claims. The author argues that this choice is more apparent than real, however. Recent developments in statute and case law force employees who chose to take their human rights claims before statutory tribunals to abandon any the additional rights which may flow from their collective agreements, making grievance arbitration the only practical option in almost all cases where it is available. To remove confusion and ambiguity, Canadian legislatures should provide clear direction on available forums. The author argues that arbitration should be confirmed as the exclusive forum for human rights claims linked to collective agreements; recourse to statutory human rights tribunals should be available only in the infrequent case where arbitration is not appropriate because the employee's union has played an active role in the alleged discrimination.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.028 | 0.017 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".