A Tale of Three Tomatoes: The New Food Economy in Toronto, Canada
Bibliographic record
Abstract
Abstract: Drawing upon research from a cluster and innovation systems perspective, we counter the argument that the food industry is a mature and dying industry and point to evidence of a vibrant, dynamic food sector that has made a substantial contribution to regional growth. Since the mid‐1990s, the most dynamic component of the Toronto urban food economy has been the small‐ and medium‐sized enterprises, comprised mainly of specialty, local, ethnic, and organic food‐processing firms that are thriving in response to consumers' demands for high‐quality, local, fresh ethnic and fusion cuisine. However, these newer firms face challenges, and our results raise the question about how a more stimulating innovative milieu can be created for them. In answer to this question, we suggest multiscaled approaches to cluster formation and policy and discuss the implications of our research for theories of innovation systems, firms, city creativity, and governance. We situate this “new food economy” within the core literature of economic geography, seeking to relocate the “agrifood” literature away from a traditional rural setting to a dynamic city‐region context, underscoring the essential role of the consumption side of agrifood chains. Moreover, we use the food sector as a lens through which to argue that mature sectors and “ordinary” activities in a city are every bit as important to the long‐term health, viability, and sustainability of a city‐region economy.
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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 | 0.003 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".