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Record W1858226755 · doi:10.29173/cjs8025

Parental Traffic Safeguarding at School Sites: Unequal Risks and Responsibilities

2011· article· en· W1858226755 on OpenAlexafffundvenueabout
Arlene Tigar McLaren, Sylvia Parusel

Bibliographic record

VenueThe Canadian Journal of Sociology · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSafeguardingSociologyCorporate governanceHegemonyEnforcementInequalityPublic relationsCriminologyPolitical scienceBusinessLawPolitics

Abstract

fetched live from OpenAlex

Based on a comparison of two public elementary schools located on the east and west sides of Vancouver, British Columbia, the paper explores the effects of spatial and social contexts on parents’ school traffic safety practices. By taking into account the dynamics of gender and social class in different geographies of mobility at the two schools, we illustrate how parents’ (especially mothers’) daily concerns, practices and volunteerism reflect unequal risks and responsibilities in safeguarding children from motorized traffic. We also suggest that despite geographical differences and social inequalities, auto-centred environments and traffic safety governance create remarkably similar parental mobility concerns at the two schools, reflecting the stratifying effects of automobility. Our analysis of the troubling effects of the automobility system underscores the importance of acknowledging how parental traffic safety practices contribute to the illusion of traffic safety and to the necessity of challenging auto hegemony.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
Threshold uncertainty score0.645

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0070.006
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.001
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 head0.132
GPT teacher head0.324
Teacher spread0.192 · 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

Citations12
Published2011
Admission routes4
Has abstractyes

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