The Changing Spatial Organization of Ethnic Retailing: Chinese and South Asian Grocery Retailers in Toronto
Bibliographic record
Abstract
Understanding the changing spatial structure of ethnic grocery retailing in Canadian urban regions provides insights into ethnic business development and the well-being of residents, particularly relating to the accessibility to healthy food options. This study explores this through a case study of Chinese and South Asian grocery retailing in the Toronto Census Metropolitan Area, focusing on the changing spatial relationship between ethnic grocery business distribution, ethnic residential patterns in 2001 and 2016. The paper highlights the varying levels of accessibility to co-ethnic and mainstream retailers for Chinese and South Asian residents in Toronto. An integrated marginalization-accessibility index is developed to reveal the spatial mismatch between the level of material deprivation and grocery store access for Chinese and South Asian residents. The results of the study show that Chinese and South Asian grocery retailers and residents have suburbanized over the study period. Index results indicate that some census tracts experienced limited access to both mainstream and ethnic grocery stores, particularly among the South Asian community. There is a growing proportion of areas that are relatively well-serviced by Chinese and South Asian grocery but exhibit a limited presence of mainstream retailers, with ethnic grocers filling market gaps not served by mainstream retailers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".