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Record W2489291430 · doi:10.1177/0093854816660144

Situational Precipitators and Interactive Forces in Sexual Crime Events Involving Adult Offenders

2016· article· en· W2489291430 on OpenAlexaboutno aff
Benoît Leclerc, Richard Wortley, Christopher Dowling

Bibliographic record

VenueCriminal Justice and Behavior · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsnot available
FundersAustralian Research Council
KeywordsSituational ethicsPsychologyCrime preventionSex offenseHuman factors and ergonomicsPoison controlInjury preventionCriminologySocial psychologyClinical psychologySexual abuseEnvironmental healthMedicine

Abstract

fetched live from OpenAlex

We investigated the role of situational precipitators in sexual offenses in relation to the use of physical force by offenders, penetration of the victim, and physical injuries to the victim. We used self-report data obtained from a Canadian sample of 553 incarcerated adult male sexual offenders. All data used in this study were gathered through a semi-structured interview conducted with each participant. First, we found that 75.8% of sexual crime events were somehow precipitated, or characterized, by the presence of precipitators before crime. Second, the relationship between each precipitator and the type of offense was statistically significant except for one precipitator. Third, although a number of precipitators were associated with the dependent variables, we also found two interaction effects that illuminated the complexity of the role of precipitators in sexual offenses. Interaction analysis can increase our understanding of sexual crime events and better inform prevention practices, such as relapse prevention.

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 score0.247
Threshold uncertainty score0.304

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 head0.059
GPT teacher head0.363
Teacher spread0.304 · 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

Citations10
Published2016
Admission routes1
Has abstractyes

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