Could Toronto Provide 10% of its Fresh Vegetable Requirements from Within its Own Boundaries? Part II, Policy Supports and Program Design
Bibliographic record
Abstract
Urban agriculture in Toronto largely focuses on self-provisioning, but it could be scaled up significantly. Our findings in an earlier paper indicate that the supply of land is not an insurmountable barrier. Rather, other more subtle impediments exist, including taxation systems and structures that assume agriculture is a strictly rural activity; inadequate sharing of knowledge among urban producers; limited access to soil, water, and seeds; and the lack of incentives to attract landowners and foundations to provide financial or in-kind support.The potential exists to develop urban agriculture so that it supplies 10% of the city's commercial demand for fresh vegetables. Scaling up to this level requires significant policy and program initiatives in five key areas: Increasing urban growers' access to spaces for production; putting in place the physical infrastructure and resources for agriculture; integrating local food production into the food supply chain; creating systems for sharing knowledge; and creating new models for governance, coordination, and financing. Our recommendations, while focusing on Toronto, offer lessons for those currently attempting to strengthen urban agriculture in other cities.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".