Longitudinal Associations in Youth Involvement as Victimized, Bullying, or Witnessing Cyberbullying
Bibliographic record
Abstract
Although cyberbullying has been linked to cyber victimization, it is unknown whether witnessing cyberbullying impacts and is impacted by experiences of cyberbullying and victimization. In the current study, we examine the frequency of youth involved as victimized, bullying, and witnessing cyberbullying and how these experiences are associated across three academic years. Participants comprised 670 Canadian students who began the longitudinal study in grades 4, 7, or 10 at Time 1 (T1). Cyber witnessing represented the largest role of youth involvement in cyberbullying. Cyber witnessing was positively associated with both cyberbullying and victimization. Cyber victimization at T1 was positively associated with cyber witnessing at T2, which was positively related to both cyberbullying and victimization at T3. Findings highlight the significance of addressing the role of cyber witnesses in cyberbullying prevention and intervention efforts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".