Organizing community-based research knowledge between universities and communities: lessons learned
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.046 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.011 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it