Community-University Partnerships for Local Impact: Advancing Sustainability Through Place-Based Education
Bibliographic record
Abstract
Due to growing demands for experiential learning opportunities and the desire to better prepare students for “real-world” decision-making, community-university partnerships have become novel and experimental spaces within postsecondary institutions. This case study explores a 3-year community-academic partnership pilot designed to provide place-based education experiences around sustainability in an urban North American context. Students’ experiences in the pilot program were examined using document reviews, interviews, and online surveys. This paper reports on student learning in relation to civic engagement and place-based education. It also explores how community-academic partnerships can be leveraged to advance sustainability goals at the municipal level, based on a framework encompassing cultural, social, and environmental dimensions of sustainability. The results suggest that the community-academic partnership provided important place-based learning opportunities that both fostered civic engagement and enabled the application of ideas about activism and action competency to support the municipality’s holistic sustainability goals. Further analysis of the pilot applying a set of principles related to community-academic partnerships is used to draw insights and identify both the benefits and challenges of the partnership for the students, faculty, and municipal partners involved.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".