What are the Risks? From Bullying in Youth to Cyberbullying in Emerging Adulthood
Bibliographic record
Abstract
Objective: Cyberbullying is a complex phenomenon, and the risk factors associated with cyberbullying are dynamic in nature.Several cyberbullying risk factors identified in the literature were examined as moderators in the relation between youth school bullying and cyberbullying in emerging adulthood.Method: Carleton University undergraduates (N = 932) were invited to complete questionnaires about their childhood experiences with traditional bullying, cyberbullying in university, impulsivity, empathy, mental health, and parental bonding.Results: Multiple regression analyses indicated that empathy significantly moderated the relation between traditional victimization and cyber victimization.Depression and anxiety symptoms significantly moderated the relation between traditional perpetration and cyber perpetration, and lastly depression symptoms significantly moderated the relation between traditional victimization and cyberperpetration.Further, exploratory three-way interaction results indicated that symptoms of depression and anxiety together moderated the relation between traditional victimization and cyber victimization.This means that participants who scored higher on depressive and anxiety symptoms, and were frequently victimized by peers in their youth, reported cyberbullying their peers more often in university.Conclusion: These results have the potential to help researchers and practitioners target relevant factors, such as empathy and mental health when developing intervention strategies.Limitations and future directions were presented.
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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 | 0.004 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".