Inuit perspectives on research ethics: The work of Inuit Nipingit
Bibliographic record
Abstract
In 2008, Inuit Tapiriit Kanatami and Inuit Tuttarvingat of the National Aboriginal Health Organization collaborated to provide input to national discussions of research ethics and processes in the Canadian Arctic. This paper describes the work of Inuit Nipingit (National Inuit Committee on Ethics and Research) during two years from 2008 to 2010. The Inuit Nipingit committee was concerned with research and its ethics environment as faced by Inuit as research participants, researchers, and those being consulted on research proposals. Members of this national committee discussed Canada’s ethical guidelines for research and responded to a call for input into the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans. In an effort to support capacity building, Inuit Nipingit also produced reference materials for Inuit community members and anyone concerned in research involving Inuit.
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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 | 0.057 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.068 | 0.042 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.008 | 0.017 |
| 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".