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Record W2021282669 · doi:10.1037/h0093925

Online solicitation offenders are different from child pornography offenders and lower risk contact sexual offenders.

2011· article· en· W2021282669 on OpenAlexaff
Michael C. Seto, Jerry M. Wood, Kelly M. Babchishin, Sheri Flynn

Bibliographic record

VenueLaw and Human Behavior · 2011
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsCarleton UniversityRoyal Ottawa Mental Health Centre
Fundersnot available
KeywordsChild pornographyPsychologyPornographySex offenseJuvenile delinquencyHuman factors and ergonomicsClinical psychologyDevelopmental psychologyPoison controlSocial psychologySexual abuseMedical emergencyThe InternetMedicine

Abstract

fetched live from OpenAlex

The current study compared 38 lower risk (based on actuarial risk assessments) men convicted of contact sexual offenses against children, 38 child pornography offenders, and 70 solicitation offenders (also known as luring or traveler offenders). Solicitation and child pornography offenders were better educated than contact offenders but did not differ on other sociodemographic variables. In comparison to child pornography offenders, solicitation offenders had lower capacity for relationship stability and lower levels of sex drive/preoccupation and deviant sexual preference. Solicitation offenders were also more problematic than lower risk contact offenders on sex drive/preoccupation and capacity for relationship stability and had greater self-reported use of child pornography. Differences between groups on two actuarial risk measures, the Static-99 and the VASOR, were inconsistent. This study suggests that solicitation offenders differ in meaningful ways from lower risk contact offenders and child pornography offenders and, consequently, in risk, treatment, and supervision needs.

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)0.000
metaresearch head score (Gemma)0.003
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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.053
GPT teacher head0.291
Teacher spread0.238 · 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

Citations135
Published2011
Admission routes1
Has abstractyes

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