Indigenous Methodologies: Traversing Indigenous and Western worldviews in research
Bibliographic record
Abstract
Using Indigenous methodologies to guide a doctoral study honouring cultural traditions and protocols was integral in working with the local community. Traditional talking circles were used to create a culturally safe environment for urban Aboriginal women to talk about their health care experiences and recommend strategies for change. The methodological research process was guided and shaped by Elders and community members sharing their knowledge and stories. This fluid non-linearity and unpredictability, common in Indigenous methodologies, challenged the researcher to stay true to the methodology while simultaneously respecting cultural protocols and traditions. The successes and challenges of embracing Indigenous methodologies in the midst of academia without losing sight of respect, commitment and accountability to Indigenous peoples and the institution are offered.
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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 | 0.161 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.018 | 0.116 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.005 | 0.009 |
| 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".