Indigenous Health Research and the Non-Indigenous Researcher: A Proposed Framework for the Autoethnographic Methodological Approach
Bibliographic record
Abstract
As a non-Indigenous doctoral student involved in a community-based participatory health research project with the Southwest Ontario Aboriginal Health Access Centre (SOAHAC), I endeavour to approach my research as an ally. Yet the role of the non-Indigenous researcher in Indigenous 1 health research is one that is both supported and contested due to conflicting knowledge systems and world views. In this paper, I propose a methodological framework for the autoethnographic approach that provides an opportunity for non-Indigenous researchers to be mindful of their part in knowledge creation, to be respectful and accountable to the communities they work with, and to ultimately contribute to an increased space within health research for Indigenous knowledge and methodologies.
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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 | 0.164 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.013 | 0.093 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.009 | 0.007 |
| 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".