Developmental Trajectories of Cyber-Aggression among Early Adolescents in Canada: The Impact of Aggression, Gender, and Time Spent Online
Bibliographic record
Abstract
The objective of the present study was to examine developmental trajectories of cyber-aggression in early adolescence, as well as their relationship with predictive factors related to cyber-aggression (e.g., overt aggression, gender, and time spent online). Participants were 384 adolescents from the Lower Mainland of British Columbia, Canada who were in grade six and grade seven at Time 1 of the study (192 boys, Mage = 13.62 years, SD = 0.74 year). Three years of longitudinal data on cyber-aggression, overt aggression, and time spent online were collected via online self-report questionnaires. Findings indicated three different trajectories of cyber-aggression: (a) a low-increasing (85.7% of the sample), (b) a stable trajectory (9.3% of the sample), and (c) a high-decreasing trajectory (4.9% of the sample). Adolescents who reported higher scores on overt aggression and spent more time online were more likely to be in the stable or high-decreasing groups. These findings highlight the importance of studying subgroups regarding the developmental course of cyber-aggression in early adolescence. The implications of present study findings give insight into gender differences and overt aggression among youth to inform cyber-aggression intervention and prevention.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".