Public acceptability of proposals to manage new takeaway food outlets near schools: cross-sectional analysis of the 2021 International Food Policy Study
Bibliographic record
Abstract
Global trends indicate that takeaway food is commonly accessible in neighbourhood food environments. Local governments in England can use spatial planning to manage the opening of new takeaway outlets in ‘takeaway management zones around schools’ (known sometimes as ‘exclusion zones’). We analysed data from the 2021 International Food Policy Study to investigate public acceptability of takeaway management zones around schools. Among adults living in Great Britain (n = 3323), 50.8% supported, 8.9% opposed, and 37.3% were neutral about the adoption of these zones. Almost three-quarters (70.4%) believed that these zones would help young people to eat better. Among 16-17 year olds (n = 354), 33.3% agreed that young people would consume takeaway food less often if there were fewer takeaways near schools. Using adjusted logistic regression, we identified multiple correlates of public support for and perceived effectiveness of takeaway management zones. Odds of support were strongest among adults reporting that there were currently too many takeaways in their neighbourhood food environment (odds ratio: 2.32; 95% confidence intervals: 1.61, 3.35). High levels of support alongside limited opposition indicate that proposals for takeaway management zones around schools would not receive substantial public disapproval. Policy makers should not, therefore, use limited public support to rationalise policy inertia.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".