Cyberbullying Victimization Among Transgender and Gender-Questioning Early Adolescents
Bibliographic record
Abstract
OBJECTIVE: To determine the association between transgender or gender-questioning identity and cyberbullying victimization in a diverse national sample of early adolescents in the United States. METHODS: We analyzed cross-sectional data from the Adolescent Brain Cognitive Development Study (year 3, 2019-2021, 11-14 years old, 48.8% female, 47.6% racial and ethnic minority). Logistic regression analyses were conducted to estimate the associations between transgender or gender-questioning identity and lifetime cyberbullying victimization, adjusting for sociodemographic confounders. RESULTS: In a sample of 9989 adolescents (1.0% transgender, 1.1% gender-questioning), both transgender (odds ratio [OR] 2.24, 95% confidence interval [CI] 1.22-4.10) and gender-questioning (OR 1.91, 95% CI 1.05-3.47) adolescents had greater odds of cyberbullying victimization compared to their cisgender peers. There was no evidence of significant effect modification of the association between transgender identity and cyberbullying victimization by sex assigned at birth. CONCLUSIONS: Transgender and gender-questioning early adolescents experience higher rates of cyberbullying victimization than their cisgender peers. Future research could investigate the risk and protective factors for cyberbullying in gender minority adolescents.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".