Media violence and its effect on aggression: assessing the scientific evidence
Bibliographic record
Abstract
* Media Violence and Its Effect on Aggression: Assessing Scientific Evidence. Jonathan L. Freedman. Toronto: University of Toronto Press, 2002. 227 pp. $50 hbk. $24.95 pbk. 1999 I was approached by Motion Picture Association of America [MPAA] and asked whether I would consider conducting a comprehensive review of all research on media says Jonathan L. Freedman in Preface to Media Violence and Its Effect on Aggression. The media's cultivation of academics who disparage research showing that their products are harmful is not new: It goes back at least as far as Will Hays' lavish support of Mortimer Adler in 1930s (Adler 1977, 193-94; Vaughn 2003). Freedman, a psychology professor at University of Toronto who has never conducted his own research on media violence, claims that financial support has not affected his objectivity, although he does not hesitate to see ulterior motives in scientists and professionals who disagree with his conclusions. Freedman expresses outrage at social scientists and public health organizations for wrongly (in his view) concluding that media violence promotes aggressive behavior. His criticism essentially boils down to two arguments. The first is that professional organizations have exaggerated number of scientific studies that have been conducted on topic. The second is that a study-by-study analysis reveals that there is no consensus in findings. Freedman is correct that number of studies has sometimes been overstated. Although some organizations have cited a number as high as 3,500, recent meta-analyses have placed number between 200 and 300. Freedman explains that inflated number originally referred to all types of articles about media effects, not just scientific studies of media violence. Somehow this number was picked up by others and misapplied. Freedman considers this the worst kind of irresponsible behavior and finds use of this figure to be as sloppy as an economist saying that his research was based on data from over 150 American states! Freedman never says how many more studies he would consider necessary. If we look at research findings in other areas, however, 200 would seem quite sufficient. For example, finding that calcium intake increases bone mass is based on thirty-three studies (Welten, Kemper, Post, and van Staveren 1995); conclusion that exposure to lead results in low I.Q. scores is based on twenty-four studies (Needleman and Gatsonis 1990). The bulk of book includes a tedious, close analysis of every published scientific study of effects of media violence on aggression or desensitization that Freedman could find. (See Reference List for some he missed and more recent compelling evidence). Not surprisingly, Freedman considers many studies unconvincing. Although some of his criticisms of individual studies are justified, he seems strongly motivated to find flaws. Moreover, after giving an exhaustive explanation of research methods, he forgets one basic principle: that lack of a statistically significant difference is not same as a finding of no effect. In addition, he disputes fact that meta-analysis, which statistically combines all findings in an area and eliminates subjective interpretation of individual studies (Mann 1994), is an appropriate way to discover a research consensus. Freedman discusses two meta-analyses (Paik and Comstock 1994; Wood, Wong, and Chachere 1991) that report a clear conclusion that media violence promotes aggression, but he dismisses them. Two recent meta-analyses (Anderson and Bushman 2001; Bushman and Anderson 2001) are not included. Freedman also chooses not to cover research on media violence's effect on fear, simply claiming that the research has not provided much support for it. Why does Freedman think there is so much bias in interpretation of media violence research? …
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".