The diasporic meatscapes of the Tamil community in Toronto: how immigrants reconfigure food environments and infrastructures to secure a taste of home
Bibliographic record
Abstract
Although scholars have studied how people navigate their foodscapes, little research has addressed together the way immigrants experience and shape their food environments. This article explores how the members of the Sri Lankan Tamil diaspora eat and purchase meat in Toronto, and how they reconfigure the food infrastructures in the city. Unpacking the intertwined politics and practices of food consumption and distribution, it contributes to a dynamic and relational approach to migrant food environments. Drawing on observations and open-ended interviews with members of the Tamil community and with Tamil food entrepreneurs, I argue that, in Toronto, Tamils give specific materialities and meanings to their food environments and food practices, turning what I call “culinary affordances” into suitable meat and meatscapes for the community. Diasporic foodscapes connect different locations – real or fantasized, close or distant, endured or lamented – notably through immigrants’ quest for home specific foods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".