Predictors for Acceptance of Sexual Aggression Myths Among People Using Cyberporn: Cross-Sectional Study
Bibliographic record
Abstract
Background: Although the acceptance of sexual aggression myths (ASAM) has been intensively studied over the past 30 years, there is still a lack of conclusive information on its relationships with a given set of cognitive and behavioral variables. Objective: This study aimed to examine to what extent sexual coercion, compulsive cyberporn use (CCU), arousing cyberporn scenes, moral incongruence, impulsivity, and sexual self-esteem can predict the ASAM-assessed with the Acceptance of Modern Myths About Sexual Aggression (AMMSA) score-among people who use cyberporn. Methods: Overall, 1557 English speakers who used cyberporn at least once during the previous 6 months (mean age 33.3, SD 10.9 years; men: n=1000, 64.2%; women: n=557, 35.8%) were included in this study after completing an online questionnaire. The questionnaire included measures of the AMMSA, sexual coercion experiences including perpetration and victimization, CCU, arousing cyberporn scenes, moral incongruence, impulsivity, and sexual self-esteem. Data analytics included descriptive statistics, Pearson correlation, analysis of variance, and multivariate linear regression. Results: Male participants had significantly higher scores than their female counterparts regarding the following variables: AMMSA (t1551=12.46, P<.001), sexual violence perpetration (t1551=3.10, P<.001), CCU (t1551=8.05, P=.003), aroused by "groups with several females" porn scenes (t1551=8.71, P=.002), and age (t1551=7.11, P<.001). Female participants had significantly greater mean scores than their male counterparts regarding the following variables: sexual violence victimization (t1551=-11, P<.001), aroused by "submission" pornographic scenes (t1551=-5, P<.001), aroused by "soft" pornographic scenes (t1551=-2.40, P=.03), and aroused by "groups with several males" pornographic scenes (t1551=-5.93, P<.001). Among the male participants, AMMSA scores were significantly predicted by scores of sexual coercion (both perpetration and victimization; β=.042, P<.001 and β=.023, P=.03, respectively), CCU (β=.339, P<.001), arousing cyberporn scenes displaying humiliation and groups of males (β=.136, P=.003), sexual self-esteem (β=.164, P<.001), and moral incongruence (β=.131, P<.001). Among the female participants, AMMSA scores were significantly predicted by the scores of sexual coercion perpetration (β=.060, P<.001), CCU (β=.287, P<.001), arousing cyberporn scenes with several females (β=-.111, P=.02), sexual self-esteem (β=.188, P<.001), and age (β=.025, P<.001). Age emerged as a distinct factor in women, with older participants more inclined to accept sexual aggression myths. Conclusions: This study identified key psychological and behavioral correlates of the ASAM across genders. CCU, sexual coercion perpetration, and sexual self-esteem predicted the ASAM. Impulsivity and moral incongruence were salient among men, and age was salient among women. Gender differences emerged in arousal of pornographic content.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".