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Record W1989248680 · doi:10.1057/sj.2014.30

The effectiveness of burglary security devices

2014· article· en· W1989248680 on OpenAlexaff
Andromachi Tseloni, Rebecca Thompson, Louise Grove, Nick Tilley, Graham Farrell

Bibliographic record

VenueSecurity Journal · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsSimon Fraser University
FundersEconomic and Social Research Council
KeywordsAirport securityComputer securityComputer scienceInternet privacyBusiness

Abstract

fetched live from OpenAlex

This study measures the effectiveness of anti-burglary security devices, both individually and in combination. Data for 2008–2012 from the Crime Survey of England and Wales are analysed via the Security Impact Assessment Tool to estimate Security Protection Factors (SPFs). SPFs indicate the level of security conferred relative to the absence of security devices. It finds that, for individual devices, external lights and door double locks or deadlocks, are most effective but, counter-intuitively, burglar alarms and dummy alarms confer less protection than no security. Combinations of devices generate positive interaction effects that increase protection more than additively. In particular, combinations with door and window locks plus external lights or security chains confer at least 20 times greater protection against burglary with entry than no security. Although further research is needed, the findings are consistent with improved security playing an important role in long-term declines in burglary rates.

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)0.004
metaresearch head score (Gemma)0.022
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 score0.004
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.015
GPT teacher head0.338
Teacher spread0.323 · 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

Citations98
Published2014
Admission routes1
Has abstractyes

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