The Role of Food Tourism in Supporting Vibrant Identities and Building Education among Diverse Communities and Visitors
Bibliographic record
Abstract
Toronto, located in the province of Ontario, is the largest city in Canada and has been named one of the most diverse cities in the world. The Greater Toronto Areas (GTA)’s ethnic diversity is synonymous with culinary diversity and an increasing demand for world foods. The GTA has been home to Indigenous peoples for thousands of years and three hundred years of immigration to Ontario from all corners of the globe have created an environment of exchange that continuously alters the food and drink available in the region. Toronto continues to maintain its multicultural character while growing at a pace of around 100,000 new residents per year (Galloway, 2017). As of 2017, nearly 50% of the city’s population had a newcomer background. It is estimated that by 2031, 75% of the GTA’s population will be either immigrants or Canadian-born children of immigrants (Nakamura and Donnelly, 2017). The region’s multicultural makeup drives disruption and innovation of food systems through a vibrant and ever-evolving food scene. The diversity of this food scene is difficult to define and package into a single tourism offering. Taking the context of growing diversity in the GTA as the starting point, the primary question explored in this paper is: What role can food tourism play in supporting vibrant identities while providing learning opportunities around local food systems and cultural heritage? This question is explored through a discussion of foods produced in the rural areas around the GTA and the foods sought by diverse communities in urban centres of the GTA. Through analysis and comparison of land management and agricultural policy documents, community engagement initiatives, and current food tourism programs, this paper also considers the impact that the GTA’s cultural diversity has in shaping the future of food education and food tourism.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".