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Record W3173451784 · doi:10.22215/etd/2016-11601

Exploring the Underlying Constructs of Rape Cognition Scales and Their Relationships with Sexual Aggression

2016· dissertation· en· W3173451784 on OpenAlexaff
Anh Pham

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsMacEwan UniversityCarleton University
Fundersnot available
KeywordsPsychologyConstruct (python library)AggressionConstruct validityCognitionExploratory factor analysisScale (ratio)Developmental psychologySocial psychologyPsychometrics

Abstract

fetched live from OpenAlex

This study attempted to replicate and extend past research by examining whether the Rape Myth Acceptance Scale, RAPE Scale, and Illinois Rape Myth Acceptance are assessing the same general construct or multiple distinct constructs by entering all items into one exploratory factor analysis (EFA). Complete scores on all three measures from 191 men from the community were entered into the EFA, which resulted in one interpretable factor, suggesting that these measures were assessing the same general construct of rape cognition. The factor was also significantly correlated with self-reported measures of past and future likelihood of sexual aggression (i.e., Sexual Experiences Survey – Tactics Version – Revised and the Proclivity – Sexual Experiences Survey – Tactics Version – Revised). This suggests that the general construct assessed by measures of rape cognition is related to sexually aggressive behaviours. Possible explanations for the current findings are discussed.

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)0.006
metaresearch head score (Gemma)0.025
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 score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.228
GPT teacher head0.353
Teacher spread0.125 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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