Building Food Sovereign Campuses: A Case Study of the Campus-Community Food Groups at Concordia University
Bibliographic record
Abstract
This thesis focused on the Concordia Campus-Community Food Groups Research Project as a case study to explore how to build food sovereign campuses. While most universities in Canada are developing sustainability policies, none have yet to be considered transformative, holistic, and in-depth. Food sovereignty approaches can help university foodservices meet these higher order sustainability conditions. The research for this dissertation was performed between 2014 and 2018 using a critical-participatory-action research approach. First, we gathered information about the campus-community food system by interviewing fifty-nine food activists and searched Concordia’s archives for relevant artifacts and articles. Second, we built an online archive and created multimedia products for the archive. We uploaded over seven hundred video interview segments and designed maps of the campus-community food system. Third, we organized a public consultation with all the campus-community food groups to get feedback about our findings and discuss how to build a food sovereign campus. This thesis provides a description of the campus-community food system map, and a historical analysis of how the groups on the map came to fruition. We found a dozen food groups that produce, process, and distribute food, fight for food justice and reduce food insecurity. We also found seven historical trends that explain how the campus-community food system was created and several factors that impeded activists from successfully preventing Concordia from hiring transnational foodservice corporations. Lastly, this thesis proposes a framework that distinguishes key differences between corporate, weak sustainability, and food sovereignty approaches to university food services. Our findings suggest that a food sovereign campus is transformative, controlled by an array of campus-community partners, not run by large multinational foodservice corporations, and provides value to the campus and surrounding communities instead of externalizing social and environmental costs. While there are some issues with using food sovereignty to refer to university campuses, we hope to inspire researchers and food activists to continue to develop the framework proposed in this thesis.
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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 | 0.005 | 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 | 0.044 | 0.013 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".