CYBERAGGRESSION: THE EFFECT OF PARENTAL MONITORING ON BYSTANDER ROLES
Bibliographic record
Abstract
The digital world has created new opportunities for aggression through cyberaggression. Despite growing research interest in cyberaggression, little is known about the various bystander roles in the digital interaction. This paper examines the effect of parental monitoring practices (parental restriction, youth disclosure, and parental solicitation) on five bystander roles: aggressor-supporter, defender, help-seeker, outsider, and passive bystander. Data were derived from self-report questionnaires answered by a sample of 501 adolescents in Israel. The findings indicate that adolescents who share their experiences of cyberaggression with their parents are more likely than others to defend the cybervictim. Interaction effects were found between adolescent gender, installing warning applications, parent gender, and the aggressor-supporter role. Boys whose parents installed warning applications and whose fathers monitored their online activities were positively associated with the aggressor-supporter role, while girls who were higher aggressor-supporter reported that their parents used warning applications but did not monitor their online activities. The discussion focuses on the theoretical and practical implications of the effectiveness of parental monitoring on the cyberaggression bystander’s role.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.015 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".