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Record W3125000603

How Safe Is the Ride? Evaluation of Design and Policy Responses to Women’s Fear of Victimization and Crime

2008· article· en· W3125000603 on OpenAlexaboutno aff
Anastasia Loukaitou‐Sideris

Bibliographic record

VenueeScholarship (California Digital Library) · 2008
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsnot available
Fundersnot available
KeywordsFear of crimePublic transportAnxietyPublic opinionPerceptionSAFERAffect (linguistics)PsychologyFocus groupPoison controlRevenueSocial psychologyTransport engineeringBusinessComputer securityPolitical scienceMarketingEngineeringEnvironmental healthMedicinePolitics
DOInot available

Abstract

fetched live from OpenAlex

Fear and anxiety about personal security are important detractors from using public transit. Empirical research in different cities of the Western world has confirmed that fear about crime affects transit ridership. Surveys of the perceptions of transit passengers have revealed a number of issues related to their anxiety about personal security. For one, fear of transit is more pronounced in certain social groups than others. Gender emerges as the most significant factor related to anxiety and fear about victimization in transit environments. Almost every fear of crime survey reports that women are much more fearful of victimization than men. This fear has some significant consequences for women and leads them to utilize precautionary measures and strategies that affect their travel patterns. These range from the adoption of certain behavioral mechanisms when in public, to choosing specific routes, modal choices, and transit environments over others, to completely avoiding particular transit environments, bus stops and railway platforms, or activities (e.g., walking, bicycling) deemed as unsafe.Women’s fear of crime in public spaces has been adequately documented. Research of transit passengers’ perceptions of transit safety has also intensified in response to the recognition that anxieties about crime are impeding travel choices and affect transit ridership and revenue, and guidelines for safer cities and transit environments have been drafted. Some studies incorporate an analysis of gender differences in perceptions of safety on transit; however, the focus is not specifically on women and safety. In contrast, a small subset of studies has focused on women’s concerns and fears about personal safety in transit environments. Criminologists complain, however, that our increased knowledge about the causes of fear has not necessarily translated into nuanced policy responses tailored to the particularities of different groups and physical settings. Additionally, there remains a general lack of knowledge regarding specific female requirements for urban and transit environments. Researchers have argued that this is partly due to the imperceptibility of women and the assumption that women and men are in the same situation and have the same needs.This study focuses on the safety concerns and needs of women riders. The research tasks undertaken included: 1) A review of the literature on women’s fear in public settings; 2) a compilation of survey findings (mostly from Canada and the United Kingdom) presenting the concerns of women passengers on issues of transit safety; 3) a compilation of an inventory of strategies followed in these countries that target women’s safety; and 4) a web-based survey of U.S. transit operators to document the programs and activities they have implemented to make their systems safer for women riders as well as their assessments of the efficacy of these programs. The survey targeted all 249 transit agencies in the United States that operate at least 50 vehicles in peak period service

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.432
Threshold uncertainty score0.458

Codex and Gemma teacher scores by category

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

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 head0.023
GPT teacher head0.223
Teacher spread0.200 · 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.

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

Citations9
Published2008
Admission routes1
Has abstractyes

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