Identifying Food Swamps Based on Area-Level Socioeconomic Patterning of Retail Food Environments in Winnipeg, Canada
Bibliographic record
Abstract
A gap in Canadian food environment research concerning Winnipeg’s food swamps is addressed using geographical assessment of socio-demographic factors. Food swamp locations were identified using (1) a composite index of socioeconomic deprivation, (2) restaurant accessibility (Euclidean distance to nearest restaurant), and (3) restaurant clustering across 5740 Dissemination Blocks (DBs) in Winnipeg. Restaurants included fast food (FFR), sit-down (SDR) and coffee shop establishments combined (ALL). DBs with high deprivation levels, close restaurant access, and signi0 cant clustering of restaurants were identi0 ed as food swamps. Significant differences in restaurant access were observed between low and high socioeconomic deprivation levels, where the most socioeconomically deprived populations in Winnipeg had easier access to highly clustered restaurants. A total 3.74 km2 of Winnipeg was designated as food swamps (ALL), impacting 10,053 (1.6%) people. We conclude that a breadth of policies is required to address food security in Winnipeg, as ~65% of the food swamps coincide with food deserts or food mirages observed by Wiebe et al. (2016).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".