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Record W1986182261 · doi:10.1080/10538712.2011.571233

Sexual Offending in Adolescence: A Comparison of Sibling Offenders and Nonsibling Offenders across Domains of Risk and Treatment Need

2011· article· en· W1986182261 on OpenAlexaff
Natasha E. Latzman, Jodi L. Viljoen, Mario J. Scalora, Daniel Ullman

Bibliographic record

VenueJournal of Child Sexual Abuse · 2011
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPsychologySiblingJuvenile delinquencyPornographySexual abuseChild sexual abuseChild abuseSuicide preventionPoison controlChild pornographyClinical psychologyDevelopmental psychologyMedicineMedical emergency

Abstract

fetched live from OpenAlex

Sibling sexual offending has received limited empirical attention, despite estimates that approximately half of all adolescent-perpetrated sexual offenses involve a sibling victim. The present study addresses this gap by examining male adolescent sibling (n = 100) and nonsibling offenders (n = 66) with regard to maltreatment histories and scores on two adolescent risk/need assessment instruments, the ERASOR and YLS/CMI. Adolescents who sexually abused a sibling, versus a nonsibling, were more likely to have histories of sexual abuse and been exposed to domestic violence and pornography. There were no group differences on ERASOR and YLS/CMI scales. This study adds to the limited discourse on sibling sexual offending and the larger literature on the heterogeneity of adolescents who have sexually offended.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.070
GPT teacher head0.337
Teacher spread0.267 · 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

Citations64
Published2011
Admission routes1
Has abstractyes

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