Community-University Partnerships: Community Engagement for Transformative Learning
Bibliographic record
Abstract
Recently, various scholars have remarked that university continuing education (UCE) is moving away from one of its original core foci, that of social justice. In this article, the possible causes of this are discussed, including current political environments, the role of universities and academics in perpetuating or disrupting the status quo, and increased reliance on cost recovery and for-profit programming. Community-based participatory research as a feasible strategy for promoting UCE’s role in social justice is also presented. An example of UCE that was developed in response to existing social inequities and driven by discussions with the community is offered to demonstrate that critical voices can have an impact and that institutions of higher education can be collaborative and foster networks of relationships for learning. Finally, key points for the successful development of a UCE program that responds to critical voices and returns to social justice are shared.
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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 | 0.021 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.025 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.003 | 0.031 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 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".