Indigenous Peoples and Health Law and Policy: Responsibilities and Obligations
Bibliographic record
Abstract
Much more than the study of laws relevant to the area of medicine, Canadian Health Law and Policy draws together the legal and policy issues that are relevant to human health, and sheds new light on emerging and continuing trends. Written by Canada's leading health law scholars, the fifth edition of this unique work provides expert commentary and analysis on a wide range of emerging health law related issues. It is a vital resource for anyone seeking to understand the developing and critical issues in health law and policy.\nState governance of Indigenous health in Canada is burdened by inequitable administrative structures and policy-based arrangements which were born of eras that denied the right of Indigenous peoples to self-govern. Although no longer resting on explicitly racist premises, this governance regime remains only partially aligned with Indigenous understandings of health and well-being. Moreover, no federal entity has assumed responsibility for a national governance structure, nor have the provinces and territories committed to a comprehensive governance structure to foster Indigenous health. The result is a cumbersome series of programs and policies with varying criteria for access, even among Indigenous populations within the same region. Governance of Indigenous health lacks the foundational principles that otherwise underpin health care governance in Canada.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.030 | 0.061 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| 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".