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Record W3117437429 · doi:10.1177/1079063220981066

Disentangling Cognitions About Sexual Aggression

2020· article· en· W3117437429 on OpenAlexaff
Chloe I. Pedneault, Chantal A. Hermann, Kevin L. Nunes

Bibliographic record

VenueSexual Abuse · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsMinistry of Community Safety and Correctional ServicesCarleton UniversityPublic Safety Canada
FundersAssociation for the Treatment of Sexual Abusers
KeywordsAggressionPsychologyCognitionPsychological interventionExploratory factor analysisDevelopmental psychologySocial psychologyClinical psychologyPsychometrics

Abstract

fetched live from OpenAlex

We examined the extent to which evaluative attitudes toward sexual aggression are distinct from other cognitions regarding sexually aggressive behavior. Evaluative attitudes toward sexual aggression refer to the extent to which sexual aggression is viewed negatively or positively. In a secondary analysis of online survey data from 495 community men, exploratory factor analysis revealed that items from a measure of evaluative attitudes formed a distinct factor from items designed to measure cognitive distortions regarding rape. These findings suggest that evaluative attitudes may be distinct from cognitive distortions. Furthermore, hierarchical regression analyses indicated that evaluative attitudes explained unique variance in self-reported past sexual aggression, proclivity for sexually aggressive behavior, and likelihood to rape. If future research finds support for a causal relationship between evaluative attitudes and sexual aggression, well-established evaluative-attitude-change procedures from the social psychological literature could be adapted to address evaluative attitudes toward sexual aggression in interventions aimed at reducing sexually aggressive behavior.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.345
Threshold uncertainty score0.788

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.070
GPT teacher head0.349
Teacher spread0.279 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations9
Published2020
Admission routes1
Has abstractyes

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