Ideological and threat-based predictors of cyber violence against women and girls
Bibliographic record
Abstract
Approximately 11% of women have received some form of unwanted or offensive sexually explicit e-mails, text messages or advances on social networking sites – examples of the many gendered forms of cyber-aggression that constitute cyber violence against women and girls (cyber VAWG). The present thesis developed a cyber VAWG scale and examined sociopolitical ideologies, perceived threats, and ambivalent sexist attitudes as predictors of endorsement of and engagement in cyber VAWG. Study 1 was administered to a university sample of male gamers (n=46), and Study 2 (n=276) and Study 3 (n=6381) recruited participants from online video gaming communities. In all three studies, exploratory factor analyses suggested cyber VAWG is a unidimensional psychological construct. Further, path analysis consistently showed that greater hostile sexism predicted greater endorsement, endorsement predicted greater engagement, and greater SDO predicted greater endorsement and engagement. Implications for future research are discussed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".