Applying the 5 R’s of Indigenous Research in Practice: Graduate Student and Van Tat Gwich’in Elder Reflections in Old Crow, Yukon
Bibliographic record
Abstract
There is growing recognition that research with Indigenous communities should foster reconciliation and support self-determination. Research frameworks like the 5 R’s—Respect, Relationship, Relevance, Reciprocity, and Responsibility—can help Indigenous and non-Indigenous partners work together in a good way. In this article, the authors, guided by Elder Mary Jane Moses of the Vuntut Gwitchin First Nation, Old Crow, Yukon, reflect on the 5 R principles in the context of a graduate student’s research, and discuss ways to implement the principles into a wildlife monitoring project. We find that discussing and implementing these principles during all stages of the research process creates the space for respecful, ethical, and effective knowledge sharing between research collaborators. By sharing our experience, we hope to inspire other researchers to pause and reflect to ensure that we all conduct our research in a good way.
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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 | 0.068 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".