Cyberbullying Perpetration by Arab Youth: The Direct and Interactive Role of Individual, Family, and Neighborhood Characteristics
Bibliographic record
Abstract
This study adopts a social-ecological/contextual perspective to explore Arab youth involvement in cyberbullying perpetration. We explored the association between individual (age, gender, and impulsivity), family (socioeconomic status and parental monitoring), and community (experiencing neighborhood violence) characteristics and cyberbullying perpetration. A moderation model exploring individual, family, and context interactions was tested. A sample of 3,178 Arab students in Grades 7 to 11 completed a structured, anonymous self-report questionnaire. The findings suggest that almost 14% of the participants have cyberbullied others during the last month. Adolescent boys with high impulsivity, low parental monitoring, and who experience a high level of violence in their neighborhood are at especially high risk of cyberbullying perpetration. Parental monitoring moderated the effects of impulsivity and experiencing neighborhood violence on adolescents' involvement in perpetrating cyberbullying. Furthermore, the results show that impulsive adolescents who experience high levels of neighborhood violence are at higher risk of cyberbullying perpetration than low impulsive children who experience the same levels of neighborhood violence. The results highlight the central role parenting plays in protecting their children from involvement in cyberbullying perpetration by buffering the effects of personal and situational risk factors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".