Joint Trajectories of Peer Cyber and Traditional Victimization in Adolescence: A Look at Risk Factors
Bibliographic record
Abstract
This study aimed to identify joint trajectories of peer cyber and traditional victimization from ages 13 to 17 and individual, family, peer, and school risk factors associated with group membership. The sample was composed of 1,194 adolescents (54.2% girls). Cyber and traditional victimization were assessed at ages 13, 15, and 17. The results first revealed a low/increasing and a high/decreasing trajectories for cybervictimization and a low/decreasing and a moderate/chronic for traditional victimization. Conditional probabilities suggested that cybervictims had a high probability of being victims on school grounds, whereas traditional victims were not necessarily the target of cybervictimization. Four joint trajectory groups were also identified. With the low victimization group as the reference category, the results revealed that different sets of predictors were associated with membership in the three other joint trajectory groups. The results are discussed in relation to intervention and prevention strategies.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".