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Record W4283369273 · doi:10.1080/01944363.2022.2050280

Junk Food Accessibility After 10 Years of a Restrictive Food Environment Zoning Policy Around Schools

2022· article· en· W4283369273 on OpenAlexaboutno aff
Lindsey Soon, Jason Gilliland, Leia Minaker

Bibliographic record

VenueJournal of the American Planning Association · 2022
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
Fundersnot available
KeywordsZoningDisadvantagedEquity (law)Psychological interventionContext (archaeology)BusinessPolitical sciencePublic economicsEconomic growthGeographyPsychologyEconomics

Abstract

fetched live from OpenAlex

Problem, research strategy, and findings Zoning has been proposed as a way of reducing unhealthy food access for youth, but little research has evaluated outcomes of proposed or existing junk food bans, and even less research has considered equity implications of such zoning policies. In this simulation study, set in the Region of Waterloo, Ontario (Canada), we examined how secondary student access to fast food restaurants and convenience stores would change under such a policy over 10 years in a mid-sized Canadian municipality. Outcomes are presented by school-level advantage (derived from the proportion of students in equity-deserving subgroups: low income, students who speak English as an additional language, and students not born in Canada). Current fast food restaurant and convenience store access was higher around schools with a higher proportion of equity-deserving students, and access remained higher around these schools even after 10 years under each policy scenario. After 10 years, the mean number of fast food restaurants and convenience stores within a 1-km network distance still exceeded five unhealthy outlets for both disadvantaged and advantaged schools, which was above the threshold associated with lower junk food consumption among youth. These findings bring into question the potential effectiveness and equity implications of restrictive zoning policies aimed at protecting youth from poor-quality food environments.Takeaway for practice Planners may consider prioritizing interventions to improve the healthfulness of food environments around schools where there are large proportions of equity-deserving students, but consideration of different interventions seems warranted in this context.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.285
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2022
Admission routes1
Has abstractyes

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