Seeing the food swamp for the weeds: Moving beyond food retail mix in evaluating young people's food environments
Bibliographic record
Abstract
Nutritional health of children and youth is an increasing cause for concern in Canada. Through food and beverage messaging in multiple environments, young people develop eating behaviours with ramifications throughout their life course. Unhealthy food retailers near schools, recreation facilities, and childcare centres-key activity settings for healthy eating promotion-present repeated, compounding exposures to commercial geomarketing. Geomarketing impacts nutritional health by promoting highly processed, calorie-dense, and nutrient-poor foods and beverages across urban landscapes. While food retail mix (as a ratio of healthy to unhealthy food retailers) can be used to assess food environments at multiple scales, such measures may misrepresent young people's unique experience of these geographic phenomena. Moving beyond uniform conceptualization of food environments, new research methods and tools are needed for children and youth. We investigated young people's food environments in the major Canadian cities of Calgary and Edmonton. Using government-initiated nutrition guidelines, we categorized 55.8% of all food retailers in Calgary, and 59.9% in Edmonton as 'unhealthy'. A Bernoulli trial at the 0.05 alpha level indicated few differences in prevalence proximal to activity settings versus elsewhere in both cities, demonstrating the limited applicability of food retail mix for characterizing young people's food environments. To model unhealthy food retailers geomarketing to children and youth, we considered their proximity to multiple activity settings, using overlapping radial buffers at the 250 m, 500 m, 1000 m, and 1500 m scales. Examining young people's food environments relative to the spaces where they learn and play, we determined that as many as 895 out of 2663 unhealthy food retailers fell within 1500 m of 21+ activity settings. By conceptualizing, measuring, and problematizing these "super-proximal" unhealthy food retailers, urban planners and public health researchers can use these techniques to pinpoint unhealthy food retailers, or "weeds in the food swamp," as a critical site for healthy eating promotion in municipalities.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".