Implementing the Tri-Council Policy on Ethical Research Involving Indigenous Peoples in Canada: So, How’s That Going in Mi’kma’ki?
Bibliographic record
Abstract
The 2010 edition of the Tri-Council Policy Statement on Ethical Conduct for Research Involving Humans introduced a new chapter, titled "Research Involving the First Nations, Inuit and Métis Peoples of Canada." The goal of our study was to explore how this chapter is being implemented in research involving Mi’kmaw communities in Nova Scotia. Qualitative data from four groups—health researchers, research ethics board representatives, financial services administrators, and Mi’kmaw community health directors—revealed that while the chapter is useful in navigating this ethical space, there is room for improvement. The challenges they encountered were not insurmountable; with political will from the academy and with guidance from Indigenous community health and research leaders solutions to these barriers can be achieved.
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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 | 0.017 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.035 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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".