“Dear John”: Overriding institutional axiology by privileging Indigenous relational ethics
Bibliographic record
Abstract
Institutional ethical oversight of research involving humans conducted at Canadian universities is guided by the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans (TCPS2). Beginning in 2010, the TCPS2 included a chapter specific to research involving First Nations, Inuit, and Metis Peoples of Canada, which is intended to provide a framework for the ethical conduct of research with Indigenous communities. These institutional guidelines reflect progress in the way research is done with Indigenous communities. However, concerns remain about the ways in which these guidelines are taken up, interpreted, and operationalized by institutional research ethics boards, which include creating tensions and challenges for Indigenous scholars conducting research together with their own communities. The purpose of this paper is to describe some of the challenges and conflicting expectations we faced as an Indigenous doctoral student and non-Indigenous academic supervisor, navigating the axiological differences between institutional ethical oversight and community relational ethics with the aim of supporting other Indigenous scholars who may experience similar challenges and influencing policy change and relational engagement in ethical review processes in university settings. We outline the various critiques of institutional oversight of Indigenous research, share several examples of how we experienced the tensions and potential/actual harm that institutional power interference caused in the review process and how we worked through them, and demonstrate how, in our experience, it was not bureaucratic institutional procedures that protected community participants from risk, it was community relationships. We conclude by discussing implications and offering our suggestions for change.
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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 | 0.107 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.043 | 0.177 |
| Scholarly communication | 0.025 | 0.017 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".