Learning and teaching community based research: Three examples of teaching CBR through the arts
Bibliographic record
Abstract
This paper draws from three chapters in a new book about learning and teaching Community Based Research (CBR) where we explore key lessons from the three authors’ experiences facilitating Arts-Based approaches to CBR in community and in classroom settings. We suggest that arts-based processes are not merely fun, but somehow unnecessary activities. In a world troubled by complex, interconnected challenges – challenges that knowledge produced through Western science has played a role in creating – the arts are not secondary to the so-called real work of science. They are essential. The seemingly intractable nature of the challenges we currently face suggests we can no longer solely rely only upon tried and tested strategies and solutions. The arts offer new possibilities for collectively co-creating innovative solutions, while building empathy and understanding and tapping into our collective creative potential.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".