Putting local food on the menu: comparing the food purchasing practices of Vancouver’s Chinese and fine dining restaurants
Bibliographic record
Abstract
In recent years, the concept of local food has attracted a great amount of attention. Little is known, however, about the organization and particular characteristics of local agrifood systems in different regions. This research paper examines the extent to which Chinese and fine dining restaurants in Vancouver, British Columbia (BC), purchase products from the local BC food system. The study also explores what factors affect the food purchasing practices, marketing strategies and supply chains of the two restaurant groups. Findings are based on data from a representative sample (n=79) and self-completion survey. Three-fifths of Chinese restaurants report sourcing over 60% of their annual food purchases from BC compared to one-third of fine dining restaurants. The difference in local food purchasing practices is not simply one of ethnic and non-ethnic cuisine types and differing food cultures. Less expensive restaurants in both groups are more likely to source local food.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".