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Record W2176614957 · doi:10.5198/jtlu.2015.537

Past and Present of Active School Transportation: An Exploration of the Built Environment Effects in Toronto, Canada from 1986 to 2006

2015· article· en· W2176614957 on OpenAlexafffundabout
Raktim Mitra, Elli M Papaioannou, Khandker Nurul Habib

Bibliographic record

VenueJournal of Transport and Land Use · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of TorontoToronto Metropolitan University
FundersUniversity of Toronto
KeywordsContext (archaeology)OddsBuilt environmentTravel behaviorPsychological interventionDemographic economicsGeographyCyclingPoison controlNested logitDemographyLogistic regressionPsychologyTransport engineeringEnvironmental healthSociologyMedicineEconomicsEngineeringEconometrics

Abstract

fetched live from OpenAlex

The health benefits of walking and cycling to and from school, also called active school transportation (AST), are well documented. In the context of a declining trend in AST across the Western world, this paper examines school-travel behavior of 11-year-old children in Toronto, using multiple cross-sectional data from 1986, 1996, and 2006 Transportation Tomorrow Surveys. Results from binomial logit models suggest that school-travel distance and neighborhood built environment indeed explain some variation in the odds of AST between 1986 and 2006, and that the correlates of AST may have changed over time. Higher neighborhood block density correlated with walking/cycling in 1986. In contrast, household automobile ownership was negatively associated with AST in 2006; the effect of the built environment was relatively weak for that year. In addition, fewer children walked/cycled in 2006 compared to 1986, even when distance to school was short (<0.8 kilometers). Policy and programs should recognize the potentially changing role of travel distance to school and automobile ownership on a child’s school travel outcome. Interventions in neighborhoods with high automobile ownership should specifically focus on education and encouragement to increase AST rates.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.202
Threshold uncertainty score0.230

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.274
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2015
Admission routes3
Has abstractyes

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