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Record W2888189947 · doi:10.1080/13552600.2018.1509574

Is childhood sexual victimization associated with cognitive distortions, self-esteem, and emotional congruence with children?

2018· article· en· W2888189947 on OpenAlexafffund
Carolyn Blank, Kevin L. Nunes, Sacha Maimone, Chantal A. Hermann, Ian V. McPhail

Bibliographic record

VenueJournal of Sexual Aggression · 2018
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversity of SaskatchewanMinistry of Community Safety and Correctional ServicesCarleton University
FundersSocial Sciences and Humanities Research Council of CanadaMacEwan University
KeywordsPsychologyCommitDevelopmental psychologyCognitionClinical psychologySex offensePoison controlHuman factors and ergonomicsSexual abusePsychiatryMedical emergencyMedicine

Abstract

fetched live from OpenAlex

The current paper examined the association between childhood sexual victimization (CSV) and constructs thought to be relevant for sexual offending in secondary analyses of three samples of adult males who committed sexual offences against children (N = 16, 28, and 20). Compared to participants who reported no CSV, those who reported CSV exhibited slightly to moderately more cognitive distortions and moderately to largely less negative evaluations of sexual offending against children; slightly to moderately higher self-esteem, positive evaluation of people who commit sexual offences, and identification with people who commit sexual offences against children; and much more emotional congruence with children. Our findings suggest that CSV may be associated with variables presumed to play a role in sexual offending against children. However, given the small sample sizes and other limitations of our studies, our evidence does not permit conclusions regarding causal relationships and any novel findings require replication.

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)0.006
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 score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
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 head0.013
GPT teacher head0.283
Teacher spread0.270 · 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

Citations8
Published2018
Admission routes2
Has abstractyes

Explore more

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