Growing sustainability education through community-university engagement : a case of community-based food system study at the University of British Columbia
Bibliographic record
Abstract
This action research case study investigates community-based experiential learning (CBEL) in a large undergraduate food systems course, documenting classroom innovations, highlighting community partner experiences, and offering recommendations for long-term improvement. It evaluates how the pedagogical synergy of sustainability education, CBEL, and food system study is relevant for post-secondary sustainability transitions. The case is situated in a major Canadian university, the University of British Columbia, within the interdisciplinary Faculty of Land and Food Systems. This dissertation investigates several iterations of one required undergraduate course and the development of the embedded British Columbia Food System Project (BCFSP), which includes approximately 200 students per term working with thirty community partners. This qualitative action research incorporates case study, primary engagement by the author, and semi-structured interviews with community partners to address two specific questions. First, what innovations were developed to integrate CBEL into a large course? Second, what insights did community partners share for more effective community-university partnerships? Five innovations were identified. First, student integrators who connect numerous food system projects encourage better understanding of complex issues among the many students. Second, projects spanning multiple years maximize returns from costly relationship development. Third, community service-learning alongside community-based research advances student understanding of community and project research potential. Fourth, offering workshops for teaching teams and students supports their limited experience with CBEL. Finally, engagement protocols clarify communication pathways with community partners to not overtax their time and resources. Community partner interview analysis corroborates challenges and benefits described in previous literature, and it contributes a new insight, that universities and associated communities need each other. While community partners articulated significant cultural differences between communities and universities, they see partnerships as a way to overcome perceived disparities and offer an alternative perspective that universities and communities share needs, interests, and resources. CBEL in particular offers options that are rooted in community and place. Engaging in CBEL can empower communities to demand more from universities, such that resources flow more freely, particularly from universities to communities. A final outcome is a biomimetic metaphor that accommodates the necessary networks of reciprocity, resilience, recursivity, and non-linear resource exchange.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.032 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".