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Record W1895243018 · doi:10.1002/jip.1418

Unravelling Crime Series Patterns amongst Serial Sex Offenders: Duration, Frequency, and Environmental Consistency

2014· article· en· W1895243018 on OpenAlexaff
Nadine Deslauriers‐Varin, Éric Beauregard

Bibliographic record

VenueJournal of Investigative Psychology and Offender Profiling · 2014
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsSimon Fraser UniversityUniversité Laval
Fundersnot available
KeywordsConsistency (knowledge bases)PsychologyFlourishingCriminologySocial psychologyComputer science

Abstract

fetched live from OpenAlex

Abstract Crime linkage and the investigation of behavioural consistency amongst serial offenders has been a flourishing field of research over the past decade or so, especially with respect to serial sex offenders. The emerging research in this field has often portrayed serial sex offenders as a single, distinct, and homogeneous group. Such an assumption, however, has never been empirically examined. Using a criminal career approach and a sample of 72 serial sex offenders who have committed a total of 361 sexual assaults on stranger victims, the current study aims to examine and describe subgroups of crime series patterns amongst serial sex offenders in terms of duration and frequency of offending. The level of environmental consistency display (i.e. offender's choice of crime location and characteristics of the crime site selected) across subgroups of crime series patterns is also examined. Study findings suggest the presence and heterogeneity of crime series patterns amongst serial sex offenders, advocating for the consideration of subgroups of crime series patterns when studying serial sex offenders. Moreover, the offenders' level of environmental consistency varies across the different crime series patterns identified, allowing for the identification of subgroups of offenders showing a higher or lower level of environmental consistency. Copyright © 2014 John Wiley & Sons, Ltd.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.038
GPT teacher head0.290
Teacher spread0.252 · 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 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

Citations28
Published2014
Admission routes1
Has abstractyes

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