Examining Cross-Cultural Differences in Youth's Moral Perceptions of Cyberbullying
Bibliographic record
Abstract
Cyberbullying has captured attention around the globe with research taking place in North and South America, Europe, and Asia. However, few of these studies have compared children and adolescents from countries with diverse cultural backgrounds, with research on Middle Eastern countries remaining scarce. To examine the influence of culture, gender, and participant roles in cyberbullying (bystander vs. perpetrator) on children and adolescents' moral evaluations of hypothetical cyberbullying events, participants read and evaluated four vignettes. Three sets of data were collected in Canada (n = 100), China (n = 100), and Iran (n = 101). Participants (N = 300; 49 percent male) were between 8 and 16 years of age (M = 11.73; standard deviation = 0.76). Two vignettes considered the perspective of a perpetrator, whereas the two others considered the perspective of a bystander. A repeated-measures analysis of variance showed that youth from Iran evaluated cyberbullying events less negatively than Canadian and Chinese youth. Regardless of culture, females evaluated cyberbullying events more negatively than males. Persian youth evaluated cyberbullying less negatively than Canadian and Chinese youth. With age, participants attributed less shame to cyberbullying behaviors. However, Chinese and Persian youth attributed more hubristic pride than Canadian youth with age. Also, Canadian and Chinese children rated perpetrator behaviors more negatively than their Persian counterparts. However, bystander behaviors were similarly negatively rated across cultures. This study breaks new ground by examining moral evaluations of cyberbullying according to participant role, culture, and gender. Findings from this study may be helpful to educators and policymakers to strengthen moral and diversity education in schools to help mitigate cyberbullying events.
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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 | 0.008 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".