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Record W2084276811 · doi:10.1016/j.iatssr.2014.12.002

Walking to school in Scotland: Do perceptions of neighbourhood quality matter?

2015· article· en· W2084276811 on OpenAlexaff
E. Owen D. Waygood, Yusak O. Susilo

Bibliographic record

VenueIATSS Research · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsNeighbourhood (mathematics)PerceptionAffect (linguistics)Human factors and ergonomicsPsychologyGeographyPoison controlAttritionBuilt environmentInjury preventionTransport engineeringDemographic economicsSocial psychologyEnvironmental healthEngineeringMedicineEconomicsMathematics

Abstract

fetched live from OpenAlex

A decrease in active travel has been observed over the past years in many Western countries including Scotland. A large part of this is likely due to the greater travel distances. However, previous research has suggested that perceptions of one's neighbourhood may also affect walking levels. If parents fear crime or traffic levels, or feel that their neighbourhood is of low quality they may not let their child walk. These perceptions are subjective and may be interlinked to each other. It is important to understand which perceptions matter more than others, in order to design the most suitable policy to promote more active travel behaviour among children. Using the Scottish Household Survey, this study investigates how or whether 48 different perceptions of neighbourhood quality or 11 reasons for having chosen their house affect children walking to school. A variable attrition method was used to reduce the number of variables for modelling. When walking distance, household characteristics, and built environment are included in a binary regression model only two perceptions were found to be significant: good local shops and slow/safe traffic. Implications of the findings are discussed.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.177
GPT teacher head0.503
Teacher spread0.327 · 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.

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

Citations60
Published2015
Admission routes1
Has abstractyes

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