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Record W151826073

Reducing crime through physical modification: Evaluating the use of situational crime prevention strategies in a rapid transit environment in British Columbia

2010· dissertation· en· W151826073 on OpenAlexaboutno aff
Courtney Laurence

Bibliographic record

VenueSummit (Simon Fraser University) · 2010
Typedissertation
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsnot available
Fundersnot available
KeywordsSituational ethicsTransit (satellite)Crime preventionCriminologyEngineeringTransport engineeringBusinessPsychologyPublic transportSocial psychology
DOInot available

Abstract

fetched live from OpenAlex

Situational crime prevention and crime prevention through environmental design are strategies that reduce criminal opportunities through modification of the physical environment. Although limited, evidence suggests that these strategies are successful at reducing crime that occurs in transit environments. The rapid transit system in Vancouver, British Columbia provides a unique opportunity for evaluation of situational prevention strategies as both control and experimental groups are available for examination. 2008 crime rates at stations were used to determine if there were differences in crimes between two SkyTrain lines. Bivariate analyses found that crime rates at stations that were not designed with crime prevention techniques were not significantly related to crime rates within a 100m buffer of the station suggesting that factors outside of neighbourhood crime trends affect station crime. Multiple regression was then employed to determine if particular design characteristics are predictive of crime. Implications and areas for future research are also 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.762
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.034
GPT teacher head0.249
Teacher spread0.215 · 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 designSimulation or modeling
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

Citations2
Published2010
Admission routes1
Has abstractyes

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