Examining the transport to school patterns of New Zealand adolescents by home-to-school distance and settlement types
Bibliographic record
Abstract
Scholarship on active transport to school has largely focused on children, (large) urban areas, the umbrella term of “active transport” which considered walking and cycling together and without taking into account walking and/or cycling distance. This research examined adolescents’ patterns of transport to school in diverse settlement types and in relation to home-to-school distance in the Otago region of Aotearoa New Zealand. Patterns of transport to school by home-to-school distance, and across school locations, are described for a sample of 2,403 adolescents (age: 15.1 ± 1.4 years; 55% females) attending 23 out of 27 schools in large urban areas (n = 1,309; 11 schools), medium urban areas (n = 265; three schools), small urban areas (n = 652; four schools) and rural settings (n = 177; five schools). Empirical data were collected through an online survey, in which adolescents reported sociodemographic characteristics, travel to school, and perceptions of walking and cycling. Home-to-school distance was measured on the shortest route determined using Geographic Information Systems (GIS)-based network analysis. Transport to school patterns differed significantly by home-to-school distance and across settlement types. Profiles of different transport user groups showed significant variability in sociodemographic characteristics, family factors, average distance to school, self-reported physical activity, and perceived health. Initiatives to promote active transport and reduce reliance on car transport to school, whether to improve health and the environment or to reduce greenhouse gas emissions, need to pay closer attention to the settlement types, distance to school, and characteristics of different transport user modes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".