Cyber-aggression towards women: Measurement and psychological predictors in gaming communities
Bibliographic record
Abstract
Approximately 52% of young women report receiving threatening messages, sharing of their private photos by others without their consent, or sexual harassment online – examples of cyber-aggression towards women. A scale to measure endorsement of cyber-aggression towards women was developed to be inclusive of the many contemporary ways that women are targeted online. We examined sociopolitical ideologies (right-wing authoritarianism, social dominance orientation) and perceived threats (based on the Dual Process Motivational Model of Ideology and Prejudice, as well as Integrated Threat Theory) as predictors of endorsement of cyber-aggression towards women in three studies (Pilot Study, n=46; Study 1, n=276; Study 2, n=6381). Study 1 and 2 participants were recruited from online video gaming communities; Study 2 comprised responses collected during or after a livestream of YouTubers doing the survey went viral. The YouTubers criticized feminism and alleged that female gamers had privilege in the gaming community. In all three studies, exploratory factor analyses suggested endorsement of cyber-aggression towards women is a unidimensional psychological construct and the scale demonstrated great internal reliability. In path analyses, social dominance orientation emerged as the most consistent predictor of endorsement of cyber-aggression towards women, mediated, in part, by perceived threats.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".