Decolonizing Research Practice
Bibliographic record
Abstract
Indigenous approaches to research are fundamentally rooted in the traditions and knowledge systems of Indigenous peoples themselves, although Indigenous methodologies and methods have become both systems for generating knowledge and ways of responding to the processes of colonization. Very specific Indigenous methods emerge from language, culture, and worldview. This chapter describes two such Indigenous research approaches drawn from the work of two Indigenous scholars with their communities in Australia and Canada. Although creative and new, these approaches draw deeply from their communities and thus express and enact traditional knowledge systems in contemporary terms. This approach may result in more pertinent research, better take-up and dissemination of research results, and a general improvement in the situations of Indigenous communities and peoples.
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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 | 0.133 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.111 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".