So Long as You Have Your Health: Health Care Distribution in Canada and Proceduralist Human Rights
Bibliographic record
Abstract
Health resource distribution in Canada has been criticized for being opaque and increasingly inequitable, with its disproportionate emphasis on curative over preventive care. Yet there has been relatively little scrutiny of priority-setting in publicly funded health care in Canada from bodies monitoring the international human right to health. Recently, however, domestic health governance on the one hand, and international health and human rights on the other, have converged around the promotion of evidence-based policy, accountability, transparency and participation as drivers of more equitable distribution of health care resources. This paper tracks the paths toward this convergence. The emphasis in health policy was largely driven by cost containment and service integration concerns, while democratic and institutional concerns around socio-economic rights protection are at the root of the health and human rights shift toward proceduralist approaches. This shared emphasis nonetheless opens new terrains of struggle for human rights approaches to health care distribution in Canada, around (i) managing indeterminacy in social determinants of health; (ii) addressing power imbalances that shape how health information is produced, communicated, and acted upon and (iii) the role of fundamental normative values that limit substantive policy around health.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.026 | 0.053 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 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".