Community Supported Agriculture in the City: The Case of Toronto
Bibliographic record
Abstract
Farming in cities is gaining momentum within North American urban centers. Community supported agriculture (CSA) projects, previously viewed primarily as rural enterprises, are now starting to appear in cities, including Toronto. Urban CSAs address the new food movement's objectives as they can provide good food that is accessible, an income to those growing the food, education on how food is grown, and show the importance of environmental stewardship and the recycling of resources. We used land parcel analysis to examine the potential for vegetable CSAs in Toronto, identifying 77 parcels with a total of 1270 acres (514 hectares) of potential land for CSA farming, a large portion of which are located in the northeast part of Toronto. This represents about 1 percent of the city's surface area. From this analysis, five scenario types were constructed that could be commercially viable, and having a range of land use, zoning, institutional, and residential characteristics. There are considerable challenges, however, in their widespread implementation. Consequently, in this paper we make policy and program recommendations on how urban CSAs in Toronto might be advanced, including pilot projects, institutional linkages, program supports, training, and extension.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".