From contracts to culture: Exploring how to leverage local, sustainable food purchasing by institutions for food systems change
Bibliographic record
Abstract
In recent years, certain hospitals, schools, and campuses across Canada have shown that they can transform their practices to serve more local and sustainable food. These changes have often been led by visionary champions, and in some cases aided by supportive public policies or programs. Yet the presence of these isolated success stories has so far not proven sufficient to tip a critical mass of institutions towards sustainability. There is great potential in leveraging institutional foodservices, with an estimated $8.5 billion market sales in Canada in 2016 (fsStrategy, 2016), to shift systems towards greater sustainability. In 2014, Food Secure Canada and the McConnell Foundation launched an action-research project and embarked on a learning journey to explore two key questions: how can food service operations and procurement practices be changed to increase local, sustainable institutional procurement; and how can this work be scaled. In 2014–2016, eight institutional food projects across Canada came together as a national Learning Group. Drawing from their experiences working in different contexts and scales, our action research project identified program and policy innovations to leverage systems change. This article explores how institutions currently buy food, and reveals the systemic barriers to increasing local, sustainable food procurement. We share lessons learned about the interplay of menus, food service operations, contracts, institutional demand, and food culture that helped to overcome these barriers. We identify enabling, peer-based learning and support as particularly relevant in a national context for the scaling out, up, and deep of local, sustainable food procurement.
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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 | 0.024 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.029 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".