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Record W3044312029 · doi:10.1111/1745-9133.12513

Reducing speeding via inanimate police presence

2020· article· en· W3044312029 on OpenAlexaffabout
Rylan Simpson, Mark A. McCutcheon, Darryl Lal

Bibliographic record

VenueCriminology & Public Policy · 2020
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsCoquitlam CollegeRoyal Canadian Mounted PoliceSimon Fraser University
Fundersnot available
KeywordsIntervention (counseling)Government (linguistics)Transport engineeringComputer securityEngineeringComputer sciencePsychology

Abstract

fetched live from OpenAlex

Research Summary The present research uses data from a police‐directed field study to explore the effects of police presence on speeding in two large cities in British Columbia, Canada. As part of the study, an inanimate but realistic‐appearing police cut‐out (“Constable Scarecrow”) was strategically positioned along roadways while motorist speed was measured using a radar‐recording device. The analyses of the multisite evaluation reveal that the presence of the cut‐out can reduce speeding when deployed along arterial roadways. Policy Implications Traffic collisions are a leading cause of death and nonfatal injuries for people worldwide. A well‐documented contributor to traffic collisions is speed. Controlling speed has thus become a priority for government, police, and community groups across the world. The findings from the present research demonstrate that police can reduce speeding via their inanimate presence. This is the first known study to evaluate the effects of an inanimate but realistic‐appearing police cut‐out on motorist behavior: a sustainable, low‐cost, and easily implementable intervention for communities of all sizes in all places.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.462
Threshold uncertainty score0.983

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 communication0.0000.000
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 head0.078
GPT teacher head0.269
Teacher spread0.191 · 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 designNot applicable
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

Citations24
Published2020
Admission routes2
Has abstractyes

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