Developmental Trajectories of Cybervictimization Among Canadian Adolescents: The Impact of Socializing Online and Sharing Personal Information
Bibliographic record
Abstract
ABSTRACT The goal of the present study was to investigate the developmental trajectories of cybervictimization, as well as to identify how risk factors such as the sharing of personal information online and engaging in online socializing was related to cybervictimization from age 13 to 16 for Canadian adolescents. Participants included 354 adolescents from the Lower Mainland of British Columbia who were in Grades 6 and 7 at Wave 1 of the study (193 boys, M age = 13.65 years, SD = 0.71 year). Three years of longitudinal data on cybervictimization, sharing personal information online and time spent socializing online were collected from self‐reports surveys. Results from latent class growth analysis identified three different trajectories of cybervictimization: a moderate‐increasing trajectory (49 adolescents, 12.7% of the sample), low‐increasing trajectory (292 adolescents, 75.8% of the sample) and high‐decreasing trajectory (13 adolescents, 3.44% of the sample). Adolescents who reported higher scores on sharing personal information and socializing online were more likely to be in moderate‐increasing subgroup. This study makes a substantial contribution to our understanding of the developmental trajectories of cybervictimization in a Western context, from late childhood through to early adolescent.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".