Rape Myth Acceptance in the Digital Age: The Effects of Using Dating Apps and the Moderation Role of Gender
Bibliographic record
Abstract
Rape myth acceptance (RMA) is a crucial predictor of rape proclivity. It has been extensively analyzed for its gender differences to aid in designing clinical interventions and health programs. Although it is well known that males generally exhibit higher levels of RMA than females, the impact of digital devices, the Internet, and dating apps on RMA and how this impact differs between genders remain understudied. This study addresses these gaps by examining a sample of 647 Chinese‐speaking college students in Canada. The findings indicate that the use of dating apps is positively associated with higher RMA; male students exhibited greater RMA levels than female students; and gender moderates the impact of dating app usage, with a more elevated effect on RMA observed in male students compared to female students. The study’s limitations are discussed, including the specificity of the sample (Chinese college students in Canada) and caution against generalizing to broader populations, along with the research and policy implications of the study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".