Issues of equity and empowerment in knowledge democracy: Three community based research examples
Bibliographic record
Abstract
Participatory action research, particularly the Collaborative Inquiry methodology of Heron and Reason has great promise for knowledge democracy. However, an elegant methodology cannot always prepare the researcher for the reality of engaging with disempowered groups, or from stumbling in the unknown territory of marginalization. In this paper, I describe three examples of collaborative research, two with Aboriginal communities aimed at increasing access to health services and one with non-profit organizations aimed at increasing collaboration. Each case presented an opportunity to learn that, despite good intentions and collaborative processes, there remain issues we cannot anticipate. Our dilemmas and the ways in which my research colleagues, our community partners and I resolved the problems are described. I conclude with lessons learned from the three cases and reflections on validity, equity and empowerment.
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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 | 0.081 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.035 | 0.063 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.011 | 0.008 |
| 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".