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Record W2109723680 · doi:10.1177/0042098013519140

Do parental perceptions of the neighbourhood environment influence children’s independent mobility? Evidence from Toronto, Canada

2014· article· en· W2109723680 on OpenAlexafffundabout
Raktim Mitra, Guy Faulkner, Ron Buliung, Michelle Stone

Bibliographic record

VenueUrban Studies · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsDalhousie UniversityUniversity of TorontoToronto Metropolitan University
FundersCanadian Institutes of Health ResearchHeart and Stroke Foundation of Canada
KeywordsNeighbourhood (mathematics)GeographyDemographic economicsSociologyPerceptionPsychologyEconomic geographyDemographyRegional scienceEconomics

Abstract

fetched live from OpenAlex

Children’s independent mobility (CIM), or a child’s freedom to explore their neighbourhood unsupervised, is important for their psychological development and potentially enables daily physical activity. However, the correlates of CIM remain under-studied particularly in terms of the influence of the neighbourhood environment. Within this context, children’s independent mobility in Toronto, Canada, was examined using linear regression and ordered logit models. Findings demonstrate that a higher level of CIM was correlated with more physical activity. Parental perceptions related to neighbourhood safety, stranger danger and sociability were associated with CIM. A child’s independent mobility was also correlated with age, sex, language spoken at home and parental travel attitudes. Interventions to increase CIM should focus on enhancing the neighbourhood social environment. Increasing the independent mobility of girls and of children with diverse ethno-cultural backgrounds are also worthy of particular research and policy attention.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.025
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.004
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.015
GPT teacher head0.269
Teacher spread0.254 · 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

Citations128
Published2014
Admission routes3
Has abstractyes

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