Organizing community-based research knowledge between universities and communities: lessons learned
Bibliographic record
Abstract
This article explores teaching, learning, and research that dynamically engages students, community workers, community members, and academics in a type of knowledge organization: the practice of community-based research (CBR). This case study details a university course in which participants (i) work together in CBR activities that foster partnership between universities and agencies in the non-profit sector, particularly AIDS service organizations in the city of Vancouver, BC, (ii) build bridges between classroom- and community-grounded knowledges and personal experience, and (iii) explore the learning and ethical underpinnings of this experience. We argue that the interaction between students, professors, and community-based organizations that results from CBR and participatory action research provides a framework for community development and the transfer of knowledges, skills, and practices between communities and individuals.
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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 | 0.038 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.016 | 0.029 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.006 | 0.005 |
| 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".