wâhkôtowin: A nehiyaw Ethical Analysis of Anti-Indigenous Racism in Canadian Nursing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Indigenous peoples in the settler state of Canada face racism on a daily basis, including in their interactions with nurses and the healthcare system. Canadian Nursing consistently fails to recognize their role in continuing to perpetrate anti-Indigenous racism. Many nurses are not taught enough about Indigenous history, settler colonialism and anti-racism to be able to recognize anti-Indigenous racism in practice, let alone effectively address it. Often the western based ethical principles nurses are taught in schools are weaponized against Indigenous peoples in practice. I propose using the nehiyaw (Cree) concept of wâhkôtowin as an ethical perspective that can help nurses tackle the problem of anti-Indigenous racism.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it