The Urban Creative-Food Economy: Producing Food for the Urban Elite or Social Inclusion Opportunity?
Bibliographic record
Abstract
The food industry has always been a major generator of economic activity in the Greater Toronto Area. However, recently the innovative and creative elements of the industry have changed. Since the mid-1990s, the fastest growing segment within the industry has been small and medium-sized enterprises (SMEs). The specialty, ethnic, and organic SMEs (hereinafter referred to as the ‘creative-food’ industry) appear to be particularly innovative as they respond to consumer demand for local, fresh, ethnic, and fusion cuisine. On the basis of sixty-five interviews with food producers, processors, restaurateurs, food media, non-government organizations, government, and private sector agencies, it is suggested that this creative-food sector is thriving despite existing public policies that bias toward large-scale, industrialized agri-food firms in the region. As such, a disconnect currently exists between, on the one hand, the traditional agrifood paradigm that the government regulatory environment is promoting and, on the other hand, the locally consumer-driven food cluster that is emerging. Public policies of multiculturalism and education have done more to facilitate the unprecedented growth of this creative subcomponent of the food sector than have explicit public food-policy initiatives. However, there is still room for policy initiatives that advance the development of this dynamic sector, especially in the area of supportive infrastructure, access to health-based ethnically appropriate food, food education, and fair labour standards. Contrary to a widely held view, the creative-food industry is not just about promoting exclusive foods for the pleasure of urban elite. Rather, it offers an opportunity for a more socially inclusive and sustainable urban development model. The findings also have implications for multilevel governance in cluster formation and policy, future research on food, as well as for theories on innovation, urban creativity, and 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 | 0.001 | 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 | 0.005 | 0.019 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".