Bibliographic record
Abstract
This book brings together scholars, artists, practitioners, and community activists to explore the possibilities for — and tensions of — social justice work through collaboration between communities and the academy. Amid a widespread institutional emphasis on increased involvement and co-production with the community, what can we expect when long-established community-oriented research practices collide with the day-to-day work of activism? How should we think about the key tenets and terms of that research, and the ongoing critique of them mounted by activists, artists, and other community members? Deploying case studies from the United Kingdom, Australia, Sweden, and Canada, and taking in universities, independent research organizations, and museums and galleries, this book breaks new ground in our understanding of the possibilities, and pitfalls, of co-production.
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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 | 0.026 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.061 |
| Scholarly communication | 0.038 | 0.034 |
| Open science | 0.005 | 0.036 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".