School travel route measurement and built environment effects in models of children’s school travel behavior
Bibliographic record
Abstract
The most common form of physical activity for people of all ages is walking, thus the use of active travel modes, such as walking or cycling for school trips, can increase daily physical activity levels. School travel is one way to encourage walking and cycling on a daily basis. Much of the recent literature reports inconsistent results pertaining to how the built environment may relate to active school travel. To date, there is no consistent approach toward conceptualizing the “environment” for its measurement, and this may be partially to blame for the inconsistent results. The purpose of this paper is twofold: to examine how characteristics of the built environment might relate to mode of school travel, while testing how measurement of the environment may influence the results in terms of the shortest path or respondent reported route mapping. The results indicate that model parameter estimates vary when using these two route measurement methods. Differences in the conceptualization and measurement of the school travel environment could carry forward into misguided planning or policy interventions targeting environmental features that may actually have no influence on school travel decisions.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".