Beyond Restriction: Relationship Quality and Gender as Moderators of the Association Between Parental Restrictive Mediation and Adolescent Cyberbullying in China
Bibliographic record
Abstract
Background/Objectives: Although parental mediation has been widely recognized as a protective factor against cyberbullying, evidence regarding restrictive mediation remains inconsistent and sometimes contradictory. This inconsistency underscores the need to identify potential moderators. The present study examines whether parent–child relationship qualities (trust and alienation) and child gender moderate the associations between restrictive mediation and both cyber-aggression and cyber-victimization. Methods: Participants included 2075 adolescents (Mage = 16.50, SD = 3.11; 926 boys) from Grades 7–12 in urban China. Self-report measures assessed restrictive mediation, parent–child trust and alienation, and adolescents’ involvement in cyber-aggression and cyber-victimization. Results: Parental restrictive mediation and parent–child alienation were positively associated with adolescents’ cyber-aggression and cyber-victimization, whereas parent–child trust was negatively associated with both outcomes. Moreover, parent–child alienation significantly moderated the associations between parental restrictive mediation and cyberbullying, such that restrictive mediation predicted higher levels of cyber-aggression and cyber-victimization under conditions of greater alienation. These moderating effects were particularly pronounced among boys, whereas for girls, the association was weaker or non-significant. Conclusions: These findings suggest that inconsistencies in prior research may be explained by variations in parent–child alienation and gender. The results highlight the need to foster trust and reduce alienation in parent–child relationships, rather than relying solely on restrictive Internet control, to prevent adolescents’ involvement in cyberbullying.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".