Identifying Food Deserts in Mississauga: A Comparative Analysis of Socioeconomic Indicators
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A lack of access to healthy food has been a problem for low-income residents in many developed urban areas. Due to travel time and additional transportation costs, these residents often opt for unhealthy food rather than nutritious alternatives. This study examines the spatial distribution of food deserts in Mississauga—one of Canada’s most populous cities and a city with one of the highest diabetes rates in the Province of Ontario. Network analysis was employed to map the geographic inaccessibility to essential nutritious food, defined as residential areas that are beyond a 15-min walking distance from grocery stores. Socioeconomic indicators were integrated to identify and compare the regions that are socioeconomically disadvantaged and, therefore, most affected by food inaccessibility. The results reveal the presence of several food deserts spatially dispersed in Mississauga. The implications of these findings are discussed, with a focus on the relationship between food desert locations and the socioeconomic conditions of the affected residents. This study provides a practical, replicable approach for identifying food deserts that can be easily applied in other regions. The model developed offers valuable tools for policymakers and urban planners to address food desert issues, improving access to healthy food and positively impacting the health and well-being of affected populations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it