Anti-Black Racism in the Canadian Healthcare System: A Reckoning
Bibliographic record
Abstract
Canada is often held out by scholars as the exception to a disheartening global pattern that suggests that high levels of racial diversity in a society are incompatible with support for generous social policies (Banting et al. 2006). The explanation for this pattern is that it is a real phenomenon (rather than an artefactual one) and it can be chalked up to racist motivations that cause powerful racial groups (whites and those non-white people who ally with whites) from endorsing policies that will benefit Black and other non-white groups (Alesina et al. 2001). One of the social policies that we are most often lauded for maintaining is the Canada Health Act (1985), which mandates that the vast majority of physician and hospital services are accessible free of charge. The prevailing discourse in Canada has been that the Canada Health Act (1985) ensures equal access to healthcare among all Canadians. In addition, polling data suggest that the vast majority of Canadians believe racism is a terrible thing (Bricker and Chhim 2020). However, cases such as that of Joyce Echaquan (Nerestant 2021) who died at a hospital in Saint-Charles-Borromée, QC, as nurses looked on and mocked and demeaned her with their words, or Leonard Rodriques (Allen 2020) who was turned away from an emergency room in Toronto during the COVID-19 pandemic and died shortly after, call into serious question the narratives of an egalitarian and benevolent system, in the context of a society that publicly endorses anti-racism.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.046 | 0.021 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".