Psychological Perspectives on Media Violence
Bibliographic record
Abstract
Given the ubiquity of digital media and the thousands of hours people spend consuming digital media each year, it is hardly surprising that researchers devote considerable effort to understanding media effects. Although many other aspects of media effects have been studied (e.g., advertising, educational media, etc.), the most-studied effect in the psychological literature is that of violent media. Most of these studies have focused on effects associated with the exposure to violent television shows and films. In the 1970s, researchers converged on the conclusion that violent television increased consumers’ risk for aggressive behavior, a consensus begun by the 1972 U.S. Surgeon General’s report on television violence. It is important to note that the vast majority of this research involved aggressive behavior, not violence. Although news reports and critics often use those words interchangeably, the distinction is critically important. Research typically involves aggressive behavior, defined as any action (verbal, relational, or physical) intended to harm someone who would not wish to be harmed, as well as the precursors of aggression (e.g., emotions, attitudes, thoughts). Such measures do not constitute “violence,” an extreme subtype of physical aggression that would, if successful, cause severe bodily damage or death. As such, it is important to examine the language surrounding any claim about violent media effects carefully, to avoid falling into the trap of manipulative rhetoric. With the advent of interactive digital media, the topic of media violence has once again risen to prominence and controversy. Researchers and laypersons alike questioned whether the interactive nature of video games meant that violent games would affect people in a different way than traditional media. Although the consensus among most media researchers and public health officials is that the effects of violent video games are no different than for other violent media, misconceptions and misrepresentations of the research persist. Those motivated to believe that violent content effects do not exist narrowly focus on the limitations of individual studies or measures and rely on studies that themselves suffer from serious methodological limitations. However, when the literature is viewed as a whole, with an emphasis on converging evidence and methods with overlapping strengths, the literature converges on the conclusion that violent video games, like violent television, are one risk factor for aggression.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".