Ethnic Grocery Retailing and Health: A Case Study of Chinese and South Asian Immigrants in Toronto
Bibliographic record
Abstract
This article investigates whether shopping at ethnic grocery retailers and the level of access to these stores relate to healthy eating and associated health outcomes among Chinese and South Asian immigrants in Toronto, Canada. Based on an online survey of 600 immigrants’ grocery behaviors and a location database of ethnic and mainstream grocery stores in Toronto, a series of accessibility measure scores were generated and linked to the survey data. Using a combination of logistic regression and chi-square analysis, this study finds that access to and the frequency of shopping at ethnic grocers are not significant factors explaining fruit, vegetable, and whole grain consumption or obesity levels among Chinese and South Asian immigrants. The research underlines the blurred lines between ethnic and mainstream grocery stores, with major retail chains increasingly catering to the growing ethnic market in Toronto. Although access and frequency of shopping were not significant, a set of demographic and socioeconomic characteristics of individuals were found to have significant relationships with healthy eating and obesity level outcomes. Further geographic research is needed to understand the spatial dynamics of ethnic grocery shopping behaviors related to immigrant well-being, including social and mental health, community life, and affirming cultural identity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".