Centering community in research - reflections on relational practices with Treaty 8 Dene First Nations, Canada
Bibliographic record
Abstract
In the field of anthropology, using the right methodology while navigating complex institutional ethics approval processes has long been considered key to conducting meaningful research with Indigenous communities. But what about community partners? How and to what extent are they involved in co-designing the research process, including defining research questions and co-developing methods? Drawing on several years of fieldwork with two Treaty 8 Dene First Nations in Arctic and subarctic Canada, this contribution offers a novel perspective on methodological approaches for non-Indigenous researchers performing community-based participatory research with Indigenous communities. By examining their experiences with Dene communities alongside literature on good practices in research with Indigenous Peoples, the authors argue that meaningful research methodology stems from a process of building trusting relationships. In ethnographic research, researchers’ positionality, their role within the community, and the potential downsides of their presence are important considerations that should inform methodological design. By questioning conventional approaches to decision-making about methodology and methods, this contribution extends emerging literature on ethnographic fieldwork. It does so by expanding on the importance of performing work based on the concept of reciprocity, while acknowledging the impact that non-reciprocal research has had on Indigenous Peoples and their ways of knowing.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.067 | 0.038 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".