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Record W2919151056 · doi:10.1177/0739456x19828444

Examining the Social and Built Environment Factors Influencing Children’s Independent Use of Their Neighborhoods and the Experience of Local Settings as Child-Friendly

2019· article· en· W2919151056 on OpenAlexafffundabout
Janet Loebach, Jason Gilliland

Bibliographic record

VenueJournal of Planning Education and Research · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicChildren's Rights and Participation
Canadian institutionsWestern University
FundersChildren’s Health FoundationCanadian Institutes of Health ResearchSocial Sciences and Humanities Research Council of CanadaChildren's Health Research InstituteHeart and Stroke Foundation of Canada
KeywordsAffordanceDestinationsPsychologyPerceptionGeographyTourism

Abstract

fetched live from OpenAlex

Neighborhoods have traditionally served as important settings for children’s independent activities, but use has declined dramatically. Global positioning system (GPS) monitors, activity diaries, annotated maps, and Google Earth–enabled interviews captured the neighborhood perceptions, usage, and independent activity ranges of twenty-three children (nine to twelve years) from London, Canada. While few participants used neighborhood settings on a habitual basis, local parks and homes of nearby friends were important independent destinations. Usage was strongly influenced by positive and negative social conditions, but local environments did not generally cater well to the shifting interests of resident children. Embedding child-friendly affordances through neighborhood planning may improve children’s experience and independent use of local settings.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.294
Threshold uncertainty score0.584

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.076
GPT teacher head0.393
Teacher spread0.317 · 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

Citations36
Published2019
Admission routes3
Has abstractyes

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