Bibliographic record
Abstract
It has been almost a quarter of a century since the Supreme Court of Canada's decision in O'Malley incorporated the concept of the duty to accommodate into Canadian human rights law and almost a decade since that concept acquired a more prominent position in that Court's adoption of the unified test for bona fide occupational requirement (BFOR) in Meiorin. Yet, I think there remains some conceptual confusion about exactly where and how the concept fits in current Canadian human rights law.\nThe duty to accommodate cannot be properly understood as a stand-alone concept. It should be seen as subsumed within the overarching concept of reasonable necessity as a critical part of the test for a BFOR. It is also inextricably bound up with the qualification of undue hardship. Moreover, a full appreciation of accommodation includes both individual and systemic dimensions. The duty to accommodate originated as an ad hoc notion, involving only after-the-fact tinkering. A full development of the concept of accommodation requires an appreciation of systemic aspects that have the potential for fundamental transformation of the world of work. To date, the systemic aspects of accommodation have been given only scant attention. In my assessment, as explored in this article, the lack of clarity on all of these points stems largely from the duty to accommodate concept not having fully escaped its roots.
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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 | 0.022 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.054 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.012 | 0.022 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".