Conducting research with Indigenous Peoples in Canada: ethical and policy considerations
Bibliographic record
Abstract
The international context of Indigenous mental health and wellbeing has been shaped by a number of key works recognizing Indigenous rights. Despite international recognitions, the mental health and wellness of Indigenous Peoples continues to be negatively affected by policies that ignore Indigenous rights, that frame colonization as historical rather than ongoing, or that minimize the impact of assimilation. Research institutions have a responsibility to conduct ethical research; yet institutional guidelines, principles, and policies often serve Indigenous Peoples poorly by enveloping them into Western knowledge production. To counter epistemological domination, Indigenous Peoples assert their research sovereignty, which for the purposes of this paper we define as autonomous control over research conducted on Indigenous territory or involving Indigenous Peoples. Indigenous sovereignty might also be applied to research impacting the landscape and the web of animal and spiritual lives evoked in a phrase such as "all my relations." This narrative review of material developed in the Canadian context examines the alignment with similar work in the international context to offer suggestions and a practice-based implementation tool to support Indigenous sovereignty in research related to wellness, mental health, and substance use. The compilation of key guidelines and principles in this article is only a start; addressing deeper issues requires a research paradigm shift.
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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 | 0.130 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.062 | 0.039 |
| Scholarly communication | 0.022 | 0.006 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.010 | 0.015 |
| 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".