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Record W4400654168 · doi:10.6000/1929-4409.2024.13.14

Truth about Rape Myths: Understanding the Effects of Sexual Violence and Date Rape Attitudes on Rape Myths Acceptance in Ghana

2024· article· en· W4400654168 on OpenAlexvenueno aff
Francis D. Boateng, Mabel Oti-Boadi, Isaac Nortey Darko

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMythologyBlamePsychologySocial psychologySexual assaultSexual violenceEthnic groupCriminologySuicide preventionPoison controlMedicinePolitical scienceMedical emergencyLawHistory

Abstract

fetched live from OpenAlex

Purpose: Rape myths acceptance is a global phenomenon with dire consequences on how sexual assault victims are perceived and treated. People who hold strong sexual violence beliefs tend to blame the victims for their victimizations while praising the offenders. It has widely been acknowledged that such attitudes hinder crime reporting while promoting violence in society. The main purpose of the current study was to examine the factors that influence rape myths acceptance among students in Ghana. Method: Data for the study was collected from students attending one of the largest universities in the country between 2021 and 2022. Results: Our results revealed that sexual violence experience, college date rape attitudes, age, degree program, and ethnicity influenced rape myths acceptance among students. Conclusion: Based on the findings, we recommend that colleges should create a conducive environment for victims of sexual violence whereby educating students about the harmful effects of rape myths acceptance.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.411
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.079
GPT teacher head0.387
Teacher spread0.308 · 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 designTheoretical or conceptual
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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

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