Making Place for Local Food: Reflections on Institutional Procurement and the Alberta Flavour Learning Lab
Bibliographic record
Abstract
Part case study, part reflective essay, this paper examines questions of place and scale in relationship to local food initiatives and, in particular, institutional procurement. A recent emphasis on “place-based” rather than “local” food systems presents an opportunity to ask, What would local food look like here? The Canadian province of Alberta is a unique place defined by a set of geographical, historical, and cultural relationships and connections around food. Through the case of the Alberta Flavour Learning Lab (Alberta Flavour), an institutional procurement initiative focused on “scaling-up” local food, we discuss how an increased emphasis on context and place activates strategic directions for thinking about food system change. We consider Alberta Flavour as a site of strategic localism that involves actively crafting a scale of local food that functions within a particular context. Rather than reinforcing divides between conventional and alternative food systems, Alberta Flavour interfaces between the broader values of the local food movement and the current realities of Alberta’s agri-food landscape and culture. We argue that the initiative’s hybrid and pragmatic approach to “getting more local food on more local plates,” while not radical, nonetheless contributes to positive food system change through “transformative incrementalism” (Buchan, Cloutier, & Friedman, in press).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".