Review of Eat local, taste global: how ethnocultural food reaches our tables
Bibliographic record
Abstract
Eat Local, Taste Global: How Ethnocultural Food Reaches our Tables, by Glen C. Filson and Bamidele Adekunle, addresses the demand, availability, and production of ethnocultural vegetables in the Greater Toronto and Hamilton Area (GTHA). The book is centered around the three largest ethnic groups in the GTHA (Chinese, South Asian, Afro-Caribbean) and considers histories of immigration, acculturation, and the availability of ethnocultural food. Taken as a whole, this book provides an overview and justification for the local production of ethnocultural vegetables. While this book is primarily based in the Southern Ontario context, there is some discussion of ethnocultural vegetable value chains in other parts of Canada and the USA. Further, Filson and Adekunle distinguish between the corporate food regime, characterized by longer value chains, and local and community level food sovereignty which are primarily discussed through farmers’ markets, community shared agriculture, and gardening. The authors cite numerous benefits of producing ethnocultural vegetables in Southern Ontario, including economic, health, social, and environmental benefits. Ethnocultural vegetables are not only fresher and more nutritious when produced locally, but there is also increased opportunity for producer-consumer contact and less food miles associated with local production.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".