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 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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.038 | 0.043 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".