Social Epidemiology of Early Adolescent Cyberbullying in the United States
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence and sociodemographic correlates of cyberbullying victimization and perpetration among a racially, ethnically and socioeconomically diverse population-based sample of 11-12-year-old early adolescents. METHODS: We analyzed cross-sectional data from the Adolescent Brain Cognitive Development (ABCD) Study (Year 2; N = 9429). Multiple logistic regression analyses were used to estimate associations between sociodemographic factors (sex, race/ethnicity, sexual orientation, country of birth, household income, parental education) and adolescent-reported cyberbullying victimization and perpetration. RESULTS: In the overall sample, lifetime prevalence of cyberbullying victimization was 9.6%, with 65.8% occurring in the past 12 months, while lifetime prevalence of cyberbullying perpetration was 1.1%, with 59.8% occurring in the past 12 months. Boys reported higher odds of cyberbullying perpetration (AOR 1.71, 95% CI 1.01-2.92) but lower odds of cyberbullying victimization (AOR 0.80, 95% CI 0.68-0.94) than girls. Sexual minorities reported 2.83 higher odds of cyberbullying victimization (95% CI 1.69-4.75) than nonsexual minorities. Lower household income was associated with 1.64 (95% CI 1.34-2.00) higher odds of cyberbullying victimization than higher household income, however household income was not associated with cyberbullying perpetration. Total screen time, particularly on the internet and social media, was associated with both cyberbullying victimization and perpetration. CONCLUSIONS: Nearly one in 10 early adolescents reported cyberbullying victimization. Pediatricians, parents, teachers, and online platforms can provide education to support victims and prevent perpetration for early adolescents at the highest risk of cyberbullying.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".