Walkability around primary schools and area deprivation across Scotland
Bibliographic record
Abstract
BACKGROUND: A number of studies based in the US, Canada, and Australia, have found evidence of associations between the built environment (BE) and mode of transport to school, and links between active travel and deprivation. Limited research in the UK compares potential BE supports for walking to school by area deprivation. Within this study, we gathered data on BE attributes previously linked to active travel, i.e., street/path connectivity, and dwelling density, created a composite 'walkability score' (WS) for areas around primary schools across urban Scotland, and explored whether poorer areas exhibit lower scores than more affluent areas, or vice versa. We consider this to be a novel approach as few studies have compared BE features by deprivation across a whole country. METHODS: Address and road/path maps were obtained and primary schools (N = 937) across mainland Scotland were mapped. Schools were attributed income deprivation scores (scores divided into quintiles (Q1: least deprived, Q5: most deprived)). Catchment area (CA) boundaries, i.e., the geographic area representing eligibility for local school attendance, were drawn around schools, and WS calculated for each CA. We compared mean WS by income quintile (ANOVA), for all local authorities (LAs) combined (N = 29), and separately for the four LAs with the greatest number of schools included in the analysis. RESULTS: For all LAs combined, the least deprived quintile (Q1) showed a significantly lower WS (-0.61), than quintiles 3, 4 and 5 (Q2: -0.04 (non-sig), Q3: 0.38, Q4: 0.09, Q5: 0.18); while for Glasgow the second least deprived quintile (Q2) showed significantly higher WS (Q1: 1.35, Q2: 1.73), than middling (Q3: 0.18) and most deprived quintiles (Q4: 0.06, Q5: -0.10). CONCLUSION: WS differ by deprivation with patterns varying depending on the spatial scale of the analysis. It is essential that less walkable areas are provided with the resources to improve opportunities to engage in active travel.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".