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Record W7127116943 · doi:10.18357/wg22201637

Travel Mode and School Catchment Area

2016· article· W7127116943 on OpenAlexafffundabout
A. E. McGowan, Geraldine J. Jordan, Jamie Spinney, David Jordan

Bibliographic record

VenueWestern Geography · 2016
Typearticle
Language
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsTrinity Western UniversitySimon Fraser University
FundersSocial Sciences and Humanities Research Council of CanadaStrong
KeywordsCatchment areaNeighbourhood (mathematics)Physical activityMode choiceMode (computer interface)Human factors and ergonomicsData collection

Abstract

fetched live from OpenAlex

Solving Canada’s childhood obesity epidemic requires a rethinking of children’s diets and levels of physical activity. Active transportation, such as walking or bicycling, is an excellent way for elementary schoolchildren to engage in regular physical activity. This study examines the factors affecting transportation mode choice decisions for the journey between home and school in general, and compares the results for two elementary schools with differently-sized catchment areas. Survey instruments were used to collect objective and subjective information from parents of schoolchildren (K-Grade 7) at two elementary schools located in Langley, BC, Canada. Results indicate similar active commuting rates for both schools and similar concerns regarding barriers to walking and bicycling to and from school. Therefore, community planners should carefully reflect on site selection and surrounding neighbourhood design to enhance safety and, thus, encourage active travel between home and school.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.838
Threshold uncertainty score0.325

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.000

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 head0.017
GPT teacher head0.275
Teacher spread0.257 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2016
Admission routes3
Has abstractyes

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