Ethical foundations and principles for collaborative research with Inuit and their governments
Bibliographic record
Abstract
Academic research in Canada involving Aboriginal peoples has changed dramatically during the last 20 years. From an academic researcher’s perspective, the changes have recently become formalised in the release of the 2nd edition of the Tri-Council Policy Statement on Ethics in Human Research. In this article we examine similarities and differences in the way ethical review is constructed and approached from university, Aboriginal and, in particular, Inuit perspectives. We begin our argument with a general comparison of research ethics as expressed in academic and Aboriginal sources in order to find areas of commonality, difference, and potential ambiguity between the two perspectives. We then briefly review our own experience with a multiyear research project involving several Inuit governments of different spatial and administrative scales. We conclude with discussion of a common issue arising from academic research, including our own work with Inuit and the research ethics board chaired by one of the authors. It concerns how to address potential tension between critical inquiry associated with Western scientific paradigms and respect and use of Inuit knowledge within a collaborative research process. In conclusion, we offer some “best practice advice” to academic researchers who face such a dilemma.
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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 | 0.231 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.031 | 0.111 |
| Scholarly communication | 0.023 | 0.007 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.014 | 0.021 |
| Insufficient payload (model declined to judge) | 0.002 | 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".