Immigrant Grocery-Shopping Behavior: Ethnic Identity versus Accessibility
Bibliographic record
Abstract
This paper contributes to the ongoing debate in the ‘new’ economic geography over the dialectic between the cultural and the economic, and in which the study of the geography of consumption is a prime example. The consumer behavior of culturally distinct immigrants is an intriguing and complex economic and cultural inquiry. In this paper we explore the grocery-shopping behavior of suburban middle-class Chinese immigrants in Toronto, where the group's ethnic economy has become full-fledged. Using a mixed approach combining focus groups and logistic modeling, we examine the preferences of Chinese immigrants between the fast-growing Chinese supermarkets and competing mainstream supermarket chains. Attention is focused upon the interplay of ethnic identity and accessibility in determining store patronage. The findings suggest a stronger effect of ethnic affinity on immigrants' choice of shopping venue than that of economic rationality. Grocery shopping, a most mundane and taken-for-granted activity, is practiced with sociocultural meanings by immigrants, and the social use of ethnic shopping spaces indicates that immigrants are not only consumers in ethnic shopping places but coactors in producing the unique ethnic retail environment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".