What Does Being a Settler Ally in Research Mean? A Graduate Students Experience Learning From and Working Within Indigenous Research Paradigms
Bibliographic record
Abstract
Research with Indigenous peoples is fraught with complexity and misunderstandings. The complexity of negotiating historical and current issues as well as the misunderstandings about what the issues really mean for individuals and communities can cause non-Indigenous researchers to shy away from working with Indigenous groups. In conducting research for my doctoral dissertation, I was a novice researcher faced with negotiating two very different sets of social contracts: the Western Canadian university’s and my Indigenous participants’. Through narrative inquiry of my experience, this article explores issues of ethics, institutional expectations, and community relationships. Guided by Kirkness and Barnhardt’s “Four R’s” framework of respect, relevance, reciprocity, and responsibility, I aimed to meet the needs of both the groups, but it was not without challenges. What do you do when needs collide? This article shares my process of negotiating the research, the decisions made, and how I came to understand my role in the process as a Settler Ally. It closes with some implications for other researchers who are considering their own roles as Settler Allies.
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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 | 0.048 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.033 | 0.072 |
| Scholarly communication | 0.022 | 0.016 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".