Media violence and its effect on aggression: assessing the scientific evidence
Notice bibliographique
Résumé
* 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? …
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».