Mapping dietary habits may provide clues about the factors that determine food choice
Bibliographic record
Abstract
BACKGROUND: Food deserts are thought to be a barrier to making healthier food choices. This concept has been challenged. The interaction between the physical environment and children's food choice has received little attention. The present study used food intake data to generate hypotheses concerning the role of the physical environment in food choice. METHODS: A cross-sectional analysis was conducted of the dietary habits of Year 5 (9-10-year-old) children from 90 of Liverpool's 118 primary schools. Individuals with the 'best' and 'worst' food choices were mapped and two areas associated with these extreme choices located. RESULTS: One thousand five hundred and thirty-five children completed the dietary questionnaire and supplied a full and valid postcode. Two adjacent areas with relatively large numbers of children in the 'best' and 'worst' food choice groups were chosen. Both areas had very similar socio-economic profiles. The contrast in the physical environments was striking, even on visual inspection. CONCLUSIONS: Food deserts as a cause of poor food choice did not stand scrutiny; the area located by the worst food choices had a plethora of shops selling food (better termed a food prairie), whereas the area located by the best food choices had no shops in evidence but did have more 'space'.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".