The Tensions of Food System Localization in Ontario's Buy-Local Procurement
Bibliographic record
Abstract
Buying local has become a recent trend within the food movement across the world. The purchasing of local foods is seen as a way to support local economies, and relationships while also promoting better environmental practices. Recently the push to buy local has extended past individual consumers to a focus on public institutions. Institutional procurement is seen to have opportunity to influence food system changes through the huge amounts of purchasing power institutions hold. At the same time, within academia there are growing critiques of the buying local trend, highlighting the limitations within food system localization. This research is an exploration of the tensions between local food procurement within public institutions and the food system localization literature. Eight semi-structured interviews were conducted of food service directors, and not-for-profit experts. Although this research is situated in Ontario, comparisons are made both out of province and out of country to demonstrate different procurement programs and thoughts towards buying local. A Marxist food justice lens is sued to analyse the potential of procurement, and its limitations for addressing food system change.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.018 | 0.013 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".