Toronto Municipal Staff and Policy-makers' Views on Urban Agriculture and Health: A Qualitative Study
Bibliographic record
Abstract
Municipal governments across the Global North are increasingly becoming key actors in shaping urban food and agriculture policy. In the City of Toronto, recent aspirational policies, such as the provincial Local Food Act and the municipal Toronto Agricultural Program, created new opportunities to shape a healthier food system. We sought municipal perspectives on the question of “How might urban agriculture policy and programs be better supported to promote equity and health?” Analysis of findings from semistructured key informant interviews with municipal staff and policy-makers (n=18) illustrated broad support for generating better quantifiable evidence of the impacts of urban agriculture on economic development and employment, health and health equity, land use and production, and partnerships and policies. Place-specific economic and equity data emerged as particularly pressing priorities. At the same time, they sought better approaches to the potential risks involved in urban agriculture. Key informants also shared their views on the use of health impact assessment research to make a case for urban agriculture to a range of stakeholders; to manage real and perceived risks; and to move beyond enabling policies to empower new investments and procedural changes that would facilitate urban agriculture expansion in the city. The results informed the evolving praxis agenda for urban agriculture at the intersections of population health, environmental sustainability, and urban governance.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.021 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".