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Record W7133027759

Deconstructing the Production of the South Asian Built Food Environment

2025· dissertation· W7133027759 on OpenAlexaboutno aff
Aisha Nur Syed

Bibliographic record

VenueTSpace · 2025
Typedissertation
Language
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationEthnic groupNewspaperProduction (economics)Food processingSouth asia
DOInot available

Abstract

fetched live from OpenAlex

Immigrant-run ethnic food retailers can provide healthy, affordable, and culturally appropriate foods to newcomer communities. They may also provide economic opportunities for immigrants that are not otherwise available. This thesis investigates the distinct relationships of different South Asian national groups with the production of the food retail environment in the adjacent regions of Scarborough and Markham, both in the Greater Toronto Area, from 1996 to 2016. To do this, emerging hot spot analysis and spatial lag panel models with time fixed effects were employed. Contextualizing quantitative results with historical newspaper articles viewed through a two-way integration process framework, this study found that different South Asian immigrant groups and retailers emerge in distinct spatio-temporal patterns and have varying relationships with the built food retail environment. Overall, this study emphasizes the cultural differences between South Asian immigrant national groups that manifest in the built environment and deconstructs the South Asian monolith.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0030.001
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.029
GPT teacher head0.306
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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