8. Jurisdictional Roulette: Constitutional and Structural Barriers to Aboriginal Access to Health
Bibliographic record
Abstract
The most important issue facing Canadian health care today is access to services. But who decides what services will be publicly funded, and how? The essays in Just Medicare explore the diverse means by which law influences what should and should not be covered by publicly-funded Medicare.\nEdited by Colleen M. Flood, the collection demonstrates three analytical approaches to the question of what services attract public funding. The first describes the existing processes for determining what is in and out of the publicly-funded sector and what is left to the private sector. The second approach suggests the principles that should guide decision-making and then investigates existing decision-making processes to see whether or not such principles are applied. The third analytical approach focuses on the processes of determining what services are publicly funded and, in particular, the right to review or appeal those decisions.\nThe role of law is usually underestimated by those in health policy. Just Medicare illustrates that legal scholars can also contribute to the issue of how to allocate scarce health resources by determining what constitutes fair processes for decision-making, and by challenging unjust processes. In re-evaluating the potential of the law, this collection adds an important new dimension to the issue of health care in Canada.\nAboriginal peoples have a unique relationship with the government of Canada that is characterized, among other things, by a complex legislative and constitutional regime. Because this regime has developed in an uneven and fractured fashion, it has resulted in jurisdictional confusion and policy vacuums regarding many aspects of Aboriginal peoples’ lives. One such aspect is the governance of matters relating to health. Improving Aboriginal health requires engaging with how jurisdictional and legislative divides underlie, shape, and govern the healthcare landscape. In this chapter, I identify and explore these divides.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".