Building Critical Community Engagement through Scholarship: Three Case Studies
Bibliographic record
Abstract
Drawing on a shared recognition that community is defined, understood, constructed, and reconstructed through contextually inflected relationships, collaborating authors use diverse interdisciplinary case studies to argue that rigorous community-engaged scholarship advances capacities for critical pursuit of cognitive and social justice. Whether through participant-centred projects undertaken with youth in government care networks, cross-cultural explorations of Indigenous and non-Indigenous science and culture as resources for food security, or facilitated dramatizations of community relations impacted by neo-liberal ideologies, contributors affirm welcoming co-learning environments that engage multiple forms of knowledge expression and mobilization. The respectful spaces held in these community-researcher collaborations enable new advances beyond hegemonic knowledge development institutionalized through colonialist histories. This essay theorizes prospects for building transformative community through scholarship, citing practical examples of the principles and practices that foster or frustrate sustainable communities. It explores the institutional arrangements and power dynamics between and among actors, asking who gets included and excluded, and what boundaries are created and crossed around complex, contradictory, and contested notions of “community.”
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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 | 0.033 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.046 | 0.034 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.007 | 0.028 |
| Research integrity | 0.010 | 0.010 |
| 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".