The legality of weight discrimination in Canada: an environmental scan of case law and the limits of Canadian legislation
Bibliographic record
Abstract
Weight stigma negatively impacts people with higher weights across the lifespan as well as social contexts and can lead to weight discrimination. As weight is not a protected identity in Canadian human rights legislation, it is important to better understand how weight discrimination is being argued in Canada's legal system. The purpose of this environmental scan was to examine and describe Canadian case law and scholarly articles pertaining to the argumentation of weight discrimination in Canada. A three-step search process was taken to identify relevant cases and articles that included; (1) Boolean keyword searches in HeinOnline, WestLaw, and LexisPlus; (2) citation searching within all results that met inclusion criteria; and (3) a keyword search in CanLII. These searches yielded a total of 33 documents that were included for analysis, including 8 scholarly articles and 25 cases. Scholarly articles highlighted consistent criticisms of existing human rights protections for higher-weight people in Canada, mostly pertaining to Limitations of disability protections. Of the 25 cases included, 16 were unsuccessful and 9 were successful, with most cases related to employment (n = 19). Our findings point to significant gaps in Canada's legal system for identifying and correcting instances of weight discrimination. Current Canadian disability protections are inadequate for those who experience weight discrimination, especially those who do not experience disability due to their weight. Our results highlight that weight ought to be a bona fide human rights issue, independent from disability protections.
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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 | 0.018 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.042 |
| Science and technology studies | 0.027 | 0.014 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".