The Changing Landscape of the Canadian Food Market: Ethnicity and the Market for Ethnic Food
Bibliographic record
Abstract
This paper explores the interplay between immigration, ethnic diversity, and food demand in Canada. Attention focuses on the consequences of an increasingly diverse population on demand for food. The notion of dietary acculturation is discussed in the context of a fragmenting market for foods in Canada. A research agenda is presented with the intent to stimulate research that addresses the role of ethnicity in shaping demand for food in Canada. As Canada becomes more ethnically diverse, the landscape of the Canadian food environment will evolve. Understanding the impact of such change on demand for foods in Canada will become increasingly important. Such understanding can help inform the supply side of the market in terms of retail and food service location decisions, as well as decisions related to the marketing mix. Dans le présent article nous analysons l’interaction entre l’immigration, la diversité ethnique et la demande alimentaire au Canada et examinons tout particulièrement les répercussions de l’accroissement de la diversité ethnique sur la demande alimentaire. Nous abordons la notion d’acculturation alimentaire dans un contexte de segmentation du marché alimentaire au Canada. Nous présentons un programme de recherche qui vise à stimuler la recherche sur le rôle de l’ethnicité dans la détermination de la demande alimentaire au Canada. Plus la population canadienne se diversifiera sur le plan ethnique, plus l’environnement alimentaire évoluera, d’où l’importance de bien déterminer les répercussions de cette diversification sur la demande alimentaire au Canada. La détermination de ces répercussions aidera les acteurs du côté de l’offre à prendre des décisions quant à l’emplacement des magasins d’alimentation et des établissements de restauration et à la logistique commerciale (marketing mix).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".