The Impact of Violent Online Games on Chinese Adolescents’ Social Relationships
Bibliographic record
Abstract
Under the Chinese internet system's anti-addiction policy, online games, particularly those with violent functions, are banned, and some previous research has only examined the harmful effects of online gaming. This study examines the parents' relationship, peer relationship, and romantic relationship levels of teenagers' social relationships from the perspective of violent online games, as well as the gender variations in social relationship positivity. Specifically, Chinese teenagers were split into the violent game and non-violent game groups, gathered the adolescents' social interaction scores in three aspects via questionnaires, and conducted a quantitative study. The data revealed that violent online games had no significant influence on teenagers' moms, peers, or romantic connections, with the exception of their ties with their fathers. This demonstrates that in families affected by violent video games, the relationships between adolescents and their parents warrants further investigation and debate in order to attain healthier parent-child relationships through the examination of the mothers' relationship pattern. In addition, peer relationship and romantic relationship scores of adolescent violent game players revealed that social behavior in violent games does not influence the development of positive social interactions in the real world. In the study of gender differences, it was determined by comparing the overall differences in social relations between males and females with the differences in the violent game group that violent games are the primary factor that boosts males' enthusiasm for social relationships. Consequently, examining the online social behavior of male online violent gamers players could be a breakthrough in enhancing the social relationships of Chinese adolescents.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".