Accommodating the Unknown: Balancing Employee Human Rights with the Employer Duty to Ensure Safety: A Dialogue on Stewart v Elk Valley and the Cannabis Act
Bibliographic record
Abstract
This paper examines the Supreme Court of Canada’s 2017 decision in Stewart v Elk Valley Coal Corporation where collective worker safety came into conflict with a worker’s addiction and his disability-related human rights. The Court’s decision continues to be relevant today, particularly following the legalization of cannabis within Canada. In this paper, the authors explore: 1) the background to the Stewart decision; 2) critical developments respecting workplace safety and substance abuse in Canada; 3) the law’s response to cannabis treatment and therapies in respect of worker safety and human rights protections; and, 4) the problematic reasoning within Stewart and what such reasoning portends for the adjudication of future human rights. The analysis reveals how the Court departed from a settled line of human rights jurisprudence by circumventing the justification test to reach a result in which higher burdens may be imposed upon workers to establish a claim of disability-related prima facie discrimination in the face of proactive drug disclosure policies, while ultimately also diminishing privacy protections for worker health conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.037 | 0.066 |
| Scholarly communication | 0.019 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.026 | 0.023 |
| Insufficient payload (model declined to judge) | 0.002 | 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".