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Record W2167339511 · doi:10.1177/1079063214544330

Are Child Abusers Sexually Attracted to Submissiveness? Assessment of Sex-Related Cognition With the Implicit Association Test

2014· article· en· W2167339511 on OpenAlexaff
Thijs Kanters, Ruud H. J. Hornsveld, Kevin L. Nunes, Jorg Huijding, Almar J. Zwets, Robert J. Snowden, Peter Muris, H.J.C. van Marle

Bibliographic record

VenueSexual Abuse · 2014
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsCarleton University
FundersErasmus+
KeywordsPsychologyHostilityImplicit-association testAggressionAssociation (psychology)Developmental psychologyCognitionAnxietyClinical psychologyInterpersonal communicationSocial psychologyPsychiatryPsychotherapist

Abstract

fetched live from OpenAlex

Child sexual abuse is associated with social anxiety, low self-esteem, and intimacy deficits. This, in combination with the core belief of a dangerous world, might suggest that child abusers are sexually attracted to submissiveness. The Implicit Association Test (IAT) was used to examine this hypothesis. Results indicated that child abusers have a stronger sexual preference for submissiveness than rapists, although there were no differences between child abusers and non-sexual offenders. Multinomial logistic regression analysis revealed that submissive-sexy associations have incremental value over child-sex associations in differentiating child abusers from other offenders. The predictive value of both implicit associations was explored by correlating IAT scores with measures for recidivism risk, aggression, and interpersonal anxiety. Child abusers with stronger child-sex associations reported higher levels of interpersonal anxiety and hostility. More research on implicit cognition in sex offenders is required for a better understanding of what these and similar implicit measures are exactly measuring and what role implicit cognition may play in sexual offending.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.293
Teacher spread0.280 · 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.

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

Citations11
Published2014
Admission routes1
Has abstractyes

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