La sécurisation culturelle est-elle suffisante pour prévenir le racisme systémique et réduire les iniquités en santé ? Regard critique sur le projet de loi 32
Bibliographic record
Abstract
INTRODUCTION: Faced with the glaring inequities between Indigenous and non-Indigenous populations, the Quebec government has proposed a bill to increase cultural safety in its health and social services for Indigenous populations. OBJECTIVE: This article aims to critically examine this bill. RESULTS: We show that it is marked by a colonial rationale. This rationale is as evident in the content of the bill as it is in its form. Regarding the content, Indigenous stakeholders were not consulted to jointly develop the bill, which fails to include any Indigenous demands. It denies the existence of systemic racism within healthcare institutions and neglects the inherent power dynamics integral to the concept of cultural safety. Regarding its form, the drafting of the bill is marked by paternalism, and the use of the "us/them" dichotomy reinforces a racialized and binary discourse between the dominant culture and Indigenous cultures. This dichotomy perpetuates ideologies of superiority and inferiority among population groups. CONCLUSION: By focusing on Indigenous cultural realities, the government sidesteps addressing the root causes of health inequities. Instead, the government should collaborate closely with Indigenous stakeholders and support policies addressing the structural determinants of health. It must also support the self-determination of Indigenous peoples.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.037 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".