Food deserts in Winnipeg, Canada: a novel method for measuring a complex and contested construct
Bibliographic record
Abstract
INTRODUCTION: "Food deserts" have emerged over the past 20 years as spaces of concern for communities, public health authorities and researchers because of their potential negative impact on dietary quality and subsequent health outcomes. Food deserts are residential geographic spaces, typically in urban settings, where low-income residents have limited or no access to retail food establishments with sufficient variety at affordable cost. Research on food deserts presents methodological challenges including retail food store identification and classification, identification of low-income populations, and transportation and proximity metrics. Furthermore, the complex methods often used in food desert research can be difficult to reproduce and communicate to key stakeholders. To address these challenges, this study sought to demonstrate the feasibility of implementing a simple and reproducible method of identifying food deserts using data easily available in the Canadian context. METHODS: This study was conducted in Winnipeg, Canada in 2014. Food retail establishments were identified from Yellow Pages and verified by public health dietitians. We calculated two scenarios of food deserts based on location of the lowest-income quintile population: (a) living ≥ 500 m from a national chain grocery store, or (b) living ≥ 500 m from a national chain grocery store or a full-service grocery store. RESULTS: The number of low-income residents living in a food desert ranged from 64 574 to 104 335, depending on the scenario used. CONCLUSION: This study shows that food deserts affect a significant proportion of the Winnipeg population, and while concentrated in the urban core, exist in suburban neighbourhoods also. The methods utilized represent an accessible and transparent, reproducible process for identifying food deserts. These methods can be used for costeffective, periodic surveillance and meaningful engagement with communities, retailers and policy makers.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".