When Research Becomes a Revolution: Participatory Action Research with Indigenous Peoples
Bibliographic record
Abstract
My reflections on Participatory Action Research (PAR) have been long and, in many ways, a test of personal endurance in all aspects of my being. In positioning myself as an Indigenous researcher in a world of contemporary academia, however, I need to go back first to my childhood where I lived and learned through an Indigenous research methodology (IRM) or paradigm. It was many years later, after having entered the professional world as a formal educator/teacher, that I ran face first into the concrete realization that my IRM was not effective as a research paradigm when I was engaged in the world of non-Aboriginal mainstream Alberta, Canada. The reasons for this have continued to unfold through the years. In looking back, I see that it was at that moment and with that realization that I was challenged with finding another way to live and work within the institutions of mainstream Canada. It was clear to me that, without a different approach to dealing with the impacts of mainstream social institutions on the social and personal lives of Aboriginal persons and communities, we would not survive as Aboriginal peoples. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.048 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.063 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".