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Record W2805205778 · doi:10.1177/0306624x18778450

An Examination of the Home-Intruder Sex Offender

2018· article· en· W2805205778 on OpenAlexaff
Katherine Amanda Ensslen, Éric Beauregard, Amélie Pedneault

Bibliographic record

VenueInternational Journal of Offender Therapy and Comparative Criminology · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsResidenceSituational ethicsPsychologySexual assaultBedroomCriminologyPoison controlInjury preventionSocial psychologyMedical emergencyDemographyMedicineGeographySociology

Abstract

fetched live from OpenAlex

One particular crime location in sexual assaults is the victim's residence. Similar to sexual burglars, "home-intruder" sex offenders choose to assault the victim in her residence, most likely in their bedroom. The aim of the current study is to analyze modus operandi, temporal factors, and victim characteristics in a sample of 347 stranger sexual assaults committed by 69 serial sex offenders to determine which factors may be more relevant to sexual assaults committed in the victim's residence compared with sexual assaults committed at another type of location. Our hypothesis is that offenders who choose to sexually assault victims in their home constitute a specific type of sex offender, one that resembles the sexual burglar. Results showed that modus operandi (e.g., burglary), temporal factors (e.g., time at crime scene with victim), and victim characteristics (e.g., age, victim-offender relationship) were significant in predicting whether the victim encounter, crime site, and victim release site were located at the victim's residence or not. Moreover, these findings were generally significant across the three crime locations, which can be explained by the high consistency in location during home-intrusion sexual assaults. Situational crime prevention strategies aimed at making a residence less attractive for offenders should help reducing this particular type of sexual assault.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.754
Threshold uncertainty score0.319

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 studies0.0000.001
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 head0.306
GPT teacher head0.416
Teacher spread0.110 · 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
Published2018
Admission routes1
Has abstractyes

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