The public plate in the transnational city: Tensions among food procurement, global trade and local legislation
Bibliographic record
Abstract
Local food systems are crucial to sustainability, and one of the most effective ways to develop them is to harness the buying power of large public institutions, such as hospitals and universities. Steering public funds toward local food systems, however, is not as easy as it might appear. Institutions must navigate a maze of regulations that can become significant barriers to effecting change. In Ontario, for example, public institutions are squeezed between two contradictory policies: the Broader Public Sector Directive, which mandates a level playing field and prohibits preferential buying based on geography, and the Local Food Act, which aims to increase the consumption of local food (with a specific focus on procurement in Ontario public institutions) and to foster successful and resilient local food economies and systems. Adding to this tension, global trade treaties are drilling down to the local level, proscribing preferential procurement of local food as “protectionist” and a barrier to trade. Public institutions are caught in the middle, wanting to purchase more local products but unwilling to risk reprisals. This paper investigates these tensions by reporting on a recent study of institutional buyers and government officials in the Toronto area to understand more thoroughly these barriers to operationalizing a local food system, while recognizing that sustainable food systems require a judicious combination of ‘local and green’ and ‘global and fair’ (Morgan 2008).
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.030 | 0.040 |
| Scholarly communication | 0.028 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".