Protection for us not “from us”: Perspectives from Cree-Anishnaabe, Dene/Métis, and Hul’q’umi’num’ physician leaders on moving beyond assumed benevolence
Bibliographic record
Abstract
The role that a government can or should play in a public health crisis or in the health of the public can only be understood by considering how it has defined its role in the past and the impacts that has caused. While many might assume that government-led public health has been net beneficial and universal in its intents and approaches across the population of Canada, the history of Indian healthcare tells a different story. We are a trio of Cree-Anishnaabe, Dene/Métis, and Hul'q'umi'num' physician leaders who believe that the role of governments in the health of the public, including during crisis, should be to protect and advance the health of all. In our experiences during the COVID-19 pandemic, we witnessed settler governments uphold historical public health paradigms that undermined the inherent rights of First Nations, Inuit and Métis Peoples. We also witnessed pockets of transformation where rights-based frameworks and anti-racist approaches were implemented that resulted in better outcomes for First Nations and Métis Peoples. We believe that for settler governments to protect and advance health for all, assumptions of exhaustive and benevolent jurisdiction over Indigenous Peoples and lands must be dismantled to create new, unfamiliar, co-governance models.
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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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.044 | 0.059 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.014 | 0.031 |
| Insufficient payload (model declined to judge) | 0.003 | 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".