“I spent the first year drinking tea”: Exploring Canadian university researchers’ perspectives on community‐based participatory research involving Indigenous peoples
Bibliographic record
Abstract
Community‐based participatory research (CBPR) is generally understood as a process by which decision‐making power and ownership are shared between the researcher and the community involved, bi‐directional research capacity and co‐learning are promoted, and new knowledge is co‐created and disseminated in a manner that is mutually beneficial for those involved. Within the field of Canadian geography we are seeing emerging interest in using CBPR as a way of conducting meaningful and relevant research with Indigenous communities. However, individual interpretations of CBPR's tenets and the ways in which CBPR is operationalized are, in fact, highly variable. In this article we report the findings of an exploratory qualitative case study involving semi‐structured, open‐ended interviews with Canadian university‐based geographers and social scientists in related disciplines who engage in CBPR to explore the relationship between their conceptual understanding of CBPR and their applied research. Our findings reveal some of the tensions for university‐based researchers concerning CBPR in theory and practice.
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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 | 0.041 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.085 | 0.073 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.007 | 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".