Classifying neighbourhoods by level of access to stores selling fresh fruit and vegetables and groceries: identifying problematic areas in the city of Gatineau, Quebec.
Bibliographic record
Abstract
OBJECTIVES: Physical access to stores selling groceries, fresh fruit and vegetables (FV) is essential for urban dwellers. In Canadian cities where low-density development practices are common, social and material deprivation may be compounded by poor geographic access to healthy food. This case study examines access to food stores selling fresh FV in Gatineau, Quebec, to identify areas where poor access is coincident with high deprivation. METHOD: Food retailers were identified using two secondary sources and each store was visited to establish the total surface area devoted to the sale of fresh FV. Four population-weighted accessibility measures were then calculated for each dissemination area (DA) using road network distances. A deprivation index was created using variables from the 2006 Statistics Canada census, also at the scale of the DA. Finally, six classes of accessibility to a healthy diet were constructed using a k-means classification procedure. These were mapped and superimposed over high deprivation areas. RESULTS: Overall, deprivation is positively correlated with better accessibility. However, more than 18,000 residents (7.5% of the population) live in high deprivation areas characterized by large distances to the nearest retail food store (means of 1.4 km or greater) and virtually no access to fresh FV within walking distance (radius of 1 km). CONCLUSION: In this research, we identified areas where poor geographic access may introduce an additional constraint for residents already dealing with the challenges of limited financial and social resources. Our results may help guide local food security policies and initiatives.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".