Arepas, Fufu, and Gado Gado: How future immigration might impact Canada's culinary landscape
Bibliographic record
Abstract
Canada’s food culture is strongly shaped by its immigrant populations. While numerous studies have explored the future of food, and others have projected the future of global migration, few, if any have combined these insights to determine how future immigration will affect the future of food. Using horizon scanning and branch analysis of emerging global and policy issues, three scenarios were generated to explore where Canada’s immigrants may come from in the next five years. These scenarios were then enriched with a horizon scan of emerging food trends to envision how these new cuisines might be adopted by Canadians in fifteen years. This research concludes with a selection of “recipes from the future” which envision a possible evolution of cuisine in Canada in an experiential manner. By anticipating these potential cuisines, this research aims to establish a shared understanding between Canadians and these future immigrant communities, helping these communities feel at home in Canada, and helping Canadians be more open to new cultures coming to the country.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".