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Record W2105466505 · doi:10.1177/0886260513487995

Implicit and Explicit Attitudes Toward Rape are Associated With Sexual Aggression

2013· article· en· W2105466505 on OpenAlexaff
Kevin L. Nunes, Chantal A. Hermann, Katie Ratcliffe

Bibliographic record

VenueJournal of Interpersonal Violence · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsAggressionPsychologyImplicit attitudeConstruct (python library)Social psychologySet (abstract data type)Human factors and ergonomicsPoison controlDevelopmental psychologyClinical psychologyMedicineMedical emergency

Abstract

fetched live from OpenAlex

We examined the relationship between self-reported sexual aggression and implicit and explicit attitudes towards rape in a sample of 86 male heterosexual university students. Large, significant group differences were found between the most sexually aggressive participants and the nonaggressive participants, with the most sexually aggressive group showing less negative implicit and explicit attitudes towards rape (Cohen's d=0.76-1.20). Implicit and explicit attitudes provided complementary information such that together they were more strongly associated with sexual aggression than on their own. The current findings suggest that implicit and explicit attitudes towards rape are associated with sexual aggression. In addition to the broader set of cognitions that appear to be assessed by most self-report measures, the narrower construct of attitudes towards rape may be a fruitful avenue of further exploration for research, assessment, and treatment of sexual aggression.

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)0.013
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.032
GPT teacher head0.317
Teacher spread0.285 · 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

Citations54
Published2013
Admission routes1
Has abstractyes

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