Systemic disruptions: decolonizing indigenous research ethics using indigenous knowledges
Bibliographic record
Abstract
Research involving and impacting Indigenous Peoples is often of little or no benefit to the communities involved and, in many cases, causes harm. Ensuring that Indigenous research is not only ethical but also of benefit to the communities involved is a long-standing problem that requires fundamental changes in higher education. To address this necessity for change, the authors of this paper, with the help of graduate and Indigenous community research assistants, undertook community consultation across their university to identify the local and national ethical needs of Indigenous researchers, communities, and Elders. This paper provides an overview of the consultation process, the themes that emerged from the consultations, and a model of the Wholistic Indigenous Research Framework that emerged.
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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 | 0.174 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.150 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.007 | 0.018 |
| 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".