Existing on a Boundary: The Delivery of Socially Uninsured Health Services to Aboriginal Groups in Canada
Bibliographic record
Abstract
Aboriginal people in Canada suffer persistent health inequalities as a result of individual and structural uncertainty. While in some nations indigenous groups remain uncounted and marginalised, in others, rationing services via an administered identity is an established process, and can result in negative health outcomes. This paper describes such outcomes, concentrating on the delivery of state-financed commercial social goods to Aboriginal groups in Canada. Two case studies are presented: the first focuses on the pharmaceutical care available to state-recognised and -eligible Aboriginal groups; and the second on Aboriginal organisations and their administrative control over programming involving commercial and non-commercial social goods. It is argued that in Canada, health inequalities maintain, in part, due to the socially unclear status of both Aboriginal individuals as citizens with specific rights, and Aboriginal authority as governance with specific decision-making power. As a result, access to health services such as pharmaceutical and dental care can be compromised. In short, individual and structural uncertainty leads to contradictions in jurisdictional oversight and governance, complicating the rights and responsibilities of all parties, hindering service delivery and potential improvements to Aboriginal 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".