Bibliographic record
Abstract
The exploration of the potential link between aggression and violent video game play has been extended to violent video game play as a precursor to violent crime. If violent video game play does increase aggression in players, that does not translate into real-world violence or violent crime. There is no single criminogenic risk factor that causes someone to commit violent criminal acts, so the idea that violent video game play causes players to commit a crime, through desensitization or otherwise, is not plausible. Therefore, this paper discusses if violent video game play is a contributing factor for an individual to participate in violent crime. The conclusion is that violent video game play and violent crime exist in a negative correlation to one another. Crime data compared to video game sales, implying higher video game play rates, shows a decrease in property and violent crime in areas where violent video game play is increased. This outcome may be explained by more time spent in the home playing video games via Routine Activities Theory as well as the presence of a catharsis effect. Since violent video game play contributes to lower crime rates, this may produce lower social costs for society as well as a heightened feeling of safety in impacted areas. Future research in this area includes violent video game play and violent crime studies conducted with a broader range of participants with various demographics, as well as the long-term effects of violent video game play on players.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".