Relationships, Rights, and Land: A Thematic Analysis of Policies for Ethical Research in Indigenous Contexts Across Canada
Bibliographic record
Abstract
Following a long history of unethical research practices by non-Indigenous academics, many Indigenous governing bodies, organizations, and communities across what is colonially called Canada have established their own policies and processes to regulate academic research involving Indigenous Peoples and Lands. Such policies reflect Indigenous community-specific, place-based ethics rooted in Indigenous laws and onto-epistemologies, affirming Indigenous Peoples’ rights to self-determination and autonomy over research that affects them. Following direction from Grand Council Treaty #3 (GCT3)—the governing body for the 28 First Nations and signatories of Treaty #3—the primary goal of my research is to support GCT3’s development of a place-based research ethics policy. Through the publication of this work, the secondary goal of this research is to provide helpful information for scholars from a broad range of academic disciplines and policymakers who are developing institutional, organizational, or governmental research policies. Through a qualitative anticolonial research methodology using a thematic document analysis, I identified and examined a total of 33 research policy documents covering a broad range of Indigenous research contexts. My analysis identified three overarching themes: (1) Relationships, (2) Rights, and (3) Land. An unexpected dataset emerged in my review of the documents: the significance of artwork. Thus, I included a discussion of how, for some Indigenous communities, artwork can be an essential aspect of place-based knowledge transmission. My findings demonstrated the importance of centring equitable and respectful research partnerships that prioritize Indigenous Peoples’ rights and onto-epistemologies—which often include ethical considerations for the Land. My conclusions underscore the need for community-engaged and Indigenous-led research to develop analysis that is firmly grounded in specific Indigenous onto-epistemologies.
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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 | 0.046 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.041 | 0.042 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".