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Record W2120724405 · doi:10.1177/0306624x03262518

The Use of Pornography during the Commission of Sexual Offenses

2004· article· en· W2120724405 on OpenAlexaff
Ron Langevin, Suzanne Curnoe

Bibliographic record

VenueInternational Journal of Offender Therapy and Comparative Criminology · 2004
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPornographyCommissionChild pornographyPsychologyCriminologyPolitical scienceThe InternetLaw

Abstract

fetched live from OpenAlex

The goal of this study was to examine the use of pornographic materials by sex offenders during the commission of their crimes. A sample of 561 sex offenders was examined. There were 181 offenders against children, 144 offenders against adults, 223 incest offenders, 8 exhibitionists, and 5 miscellaneous cases. All but four cases were men. A total of 96 (17%) offenders had used pornography at the time of their offenses. More offenders against children than against adults used pornography in the offenses. Of the users, 55% showed pornographic materials to their victims and 36% took pictures, mostly of child victims. Nine cases were involved in the distribution of pornography. Results showed that pornography plays only a minor role in the commission of sexual offenses, however the current findings raise a major concern that pornography use in the commission of sexual crimes primarily involved child victims.

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)0.008
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.421
GPT teacher head0.407
Teacher spread0.014 · 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

Citations43
Published2004
Admission routes1
Has abstractyes

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