Conducting Participatory Action Research with Canadian Indigenous Communities: A Methodological Reflection
Bibliographic record
Abstract
A central challenge with participatory action research (PAR) pertains to discrepancies between principles and practice. What sounds simple in theory (e.g., establishing a respectful collaboration) is often much more complex in real community settings. The challenges, lessons learned, and successes of PAR were examined within the context of a large national research project that involved 8 First Nation communities and academics. To engage in the process of reflective examination, two methodological approaches were utilized: (1) a qualitative interview study with 19 project members about their experiences within the project, and (2) a secondary qualitative analysis of the author’s own experiences and observations (as recorded in research journals). This paper summarizes some of the barriers to conducting PAR with Indigenous communities (i.e., themes of distrust/personal safety concerns, community readiness, waning motivation, financial stress, power differences, and differing norms/expectations) , as well as some of the lessons that were learned about how to overcome these challenges and cultivate strong, healthy research relationships.
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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 | 0.310 | 0.154 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.040 | 0.032 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.005 | 0.009 |
| 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".