An Evidence-Based Approach to Examining the Impact of Playing Violent Video and Computer Games
Bibliographic record
Abstract
Video and computer (“electronic”) games have attained lasting status as a preferred leisure activity, and games with violence often achieve great popularity. There has been wide speculation that playing violent electronic games is harmful for children, but only minimal empirical work. A body of research is needed to provide a base for media education for consumers and for policy makers. This article presents findings from a program of research. The goal of the program is to systematically accumulate data to establish relationships between playing violent electronic games and aspects of children's personality and behavior. Significant negative relationships have been identified between a preference for violent games and various outcome measures including self-perceptions of academic performance and behavior. However, such relationships are not found in every study. We have proposed that some children may be more susceptible to being affected by game-playing. In future studies, these possible “high risk” players deserve special attention. Understanding how children experience playing a violent electronic game is another question that may be critical to understanding game impact.
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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 | 0.183 | 0.403 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.041 | 0.018 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".