The Impacts of Ethnic Retail Access on Health & Well-Being Within the Chinese Newcomer Population in the GTA
Bibliographic record
Abstract
Accessible food options that are nutritional, affordable, and culturally appropriate are essential to supporting one’s mental health and well-being. The degree to which immigrants and newcomers can uphold ethnic and cultural food practices from their countries of origin within new host countries and the communities in which they settle may be an important factor in determining healthy food access. Chinese newcomer populations in the Greater Toronto Area face significant challenges accessing ethnic foods and consuming culturally appropriate foods due to many barriers. The purpose of this Major Research Paper is to examine: i) the experiences of Chinese newcomers in accessing ethnic grocery foods and culturally appropriate foods, and ii) perceived impacts on health and well-being. The findings demonstrate that Chinese newcomers face increasing barriers to accessing ethnoracial foods due to challenges related to transportation access, the lack of availability of ethnic groceries and ethnic options in mainstream stores, affordability of ethnic ingredients and the increased time it takes to access ethnic retailers. Policy leaders must recognize the need for increased ethnic grocery retailers in bridging better mental health and access to healthy foods within newcomer populations.
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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 | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".