The Changing Spatial Organization of Ethnic Retailing: A Case Study of Chinese and South Asian Grocery Store Retailers in the Toronto CMA From 2001 to 2016
Bibliographic record
Abstract
Understanding the changing spatial structure of ethnic grocery retailing in Canadian urban regions can provide insights into ethnic business development and the well-being of residents, particularly relating to the availability of healthy food and risk of nutrition-related illnesses. This study explores this through a case study of Chinese and South Asian grocery retailing in the Toronto Census Metropolitan Area (CMA). In particular, the changing spatial relationship between ethnic grocery business distribution, ethnic residential patterns, and spatial accessibility is examined between 2001 and 2016. A combination of location quotients and global and local indicators of spatial autocorrelation were utilized to assess the relationship between ethnic groups while measures of spatial central tendency and a nearest neighbor analysis assessed the distribution of grocery retailers. An integrated marginalization-accessibility index was then developed to highlight any spatial mismatch between the level of material deprivation and grocery store access, highlighting patterns of inequality throughout the CMA. The results of the study reveal that Chinese and South Asian grocery retailers and residents have suburbanized over the study period. Index results also indicate that some census tracts (CTs) experienced limited access to both mainstream and ethnic grocery stores, particularly among the South Asian community. Finally, there is a growing number of CTs that are well-serviced to Chinese and South Asian grocery stores but are under-serviced to mainstream retailers, potentially identifying areas where ethnic grocers are filling gaps in service. Key words: ethnic grocery retailing, ethnic residential patterns, accessibility, healthy food provision, marginalized neighbourhoods, Toronto Census Metropolitan Area
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".