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Record W2280197485 · doi:10.1177/1079063215594377

Offender Mobility During the Crime: Investigating the Variability of Crime Event Contexts and Associated Outcomes in Stranger Sexual Assaults

2015· article· en· W2280197485 on OpenAlexaff
Ashley N. Hewitt, Éric Beauregard

Bibliographic record

VenueSexual Abuse · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCommitSituational ethicsSexual assaultCriminologyPsychologySocial psychologyHuman factors and ergonomicsPoison controlMedical emergencyMedicineComputer science

Abstract

fetched live from OpenAlex

Using data from qualitative interviews and police reports, latent class analysis is used on a sample of 54 repeat stranger sexual offenders who committed 204 sexual assaults to identify discrete contexts present at the time of victim encounter that influence these offenders' decision to use more than one location to commit their crimes. Five distinct classes are identified: residential outdoor common area, spontaneous/quiet outdoor site, residential home, active green space, and indoor/public gathering place. An investigation into the outcome(s) that most often result from the offender's decision to move the victim during the sexual assault indicates that those who move the victim from an active green space overwhelmingly engage in sexual penetration, as well as forcing their victims to commit sexual acts on them. Crimes where the victim is moved from a residential home show evidence of the offender physically harming the victim as well as using more force than necessary to complete the assault. Implications for situational crime prevention are 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)0.004
metaresearch head score (Gemma)0.002
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 score0.047
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.082
GPT teacher head0.365
Teacher spread0.283 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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