Aboriginal urbanization and rights in Canada: Examining implications for health
Bibliographic record
Abstract
Urbanization among Indigenous peoples is growing globally. This has implications for the assertion of Indigenous rights in urban areas, as rights are largely tied to land bases that generally lie outside of urban areas. Through their impacts on the broader social determinants of health, the links between Indigenous rights and urbanization may be related to health. Focusing on a Canadian example, this study explores relationships between Indigenous rights and urbanization, and the ways in which they are implicated in the health of urban Indigenous peoples living in Toronto, Canada. In-depth interviews focused on conceptions of and access to Aboriginal rights in the city, and perceived links with health, were conduced with 36 Aboriginal people who had moved to Toronto from a rural/reserve area. Participants conceived of Aboriginal rights largely as the rights to specific services/benefits and to respect for Aboriginal cultures/identities. There was a widespread perception among participants that these rights are not respected in Canada, and that this is heightened when living in an urban area. Disrespect for Aboriginal rights was perceived to negatively impact health by way of social determinants of health (e.g., psychosocial health impacts of discrimination experienced in Toronto). The paper discusses the results in the context of policy implications and future areas of research.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.022 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".