Laying the Groundwork: A Practical Guide for Ethical Research with Indigenous Communities
Bibliographic record
Abstract
Although there are numerous ethical guidelines for research with Indigenous communities, not all research is conducted in an ethical, culturally respectful, and effective way. To address this gap, we review four ethical frameworks for research with Indigenous Peoples in Canada. Drawing upon our experiences conducting a transformative social justice research project in five Indigenous communities, we discuss the ethical tensions we have encountered and how we have attempted to address these challenges. Finally, drawing on these experiences, we make recommendations to support those planning to conduct research with Indigenous Peoples in Canada. We discuss the importance of training to highlight the intricacies and nuances of bringing the ethical guidelines to life through co-created research with Indigenous communities.
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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 | 0.134 | 0.103 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.020 | 0.033 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.013 | 0.026 |
| Insufficient payload (model declined to judge) | 0.015 | 0.013 |
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".