Common Insights, Differing Methodologies: Towards a Fusion of Indigenous Methodologies, Participatory Action Research, and White Studies in an Urban Aboriginal Research Agenda
Bibliographic record
Abstract
In this paper we discuss three broad research approaches: Indigenous Methodologies, Participatory Action Research, and White Studies. We suggest that a fusion of these three approaches can be useful, especially in terms of collaborative work with Indigenous communities. More specifically, we argue that using Indigenous Methodologies and Participatory Action Research, but refocusing the object of inquiry directly and specifically on the institutions and structures that Indigenous peoples face, can be a particularly effective way of transforming Indigenous peoples from the objects of inquiry to its authors. A case study focused on the development of appropriate research methods for a collaborative project with the Urban Aboriginal communities of the Okanagan Valley in BC, Canada, provides an illustration of the methodological fusion we propose.
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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 | 0.301 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.019 | 0.100 |
| Scholarly communication | 0.030 | 0.028 |
| Open science | 0.006 | 0.037 |
| Research integrity | 0.005 | 0.008 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".