News that Counts: Newspaper Images of Crime and Victimization Statistics
Notice bibliographique
Résumé
The study of media images of crime and victimization has tended to focus on the reporting of criminal events. However, the reporting of crime and victimization statistics is an important, if unaddressed theme in crime news coverage. Such statistics, as Joel Best and other social constructionists have argued, perform as important rhetorical devices in the social processes by which crime and (other social) problems are constructed and maintained. Such statistics are used to press claims about the pervasiveness and scope of new problems and therefore about the need for urgent social action. At the earliest stages, these claims may be issued by those who have no official status but who may be the only ones interested in the emergent issue. The legitimacy which statistics lend to social problems is relevant not only at the initial phases of construction, however. To remain on the public agenda, social problems require maintenance. And the regular diffusion of statistical information which purports to document shifts in the scope or size of the problem is essential to such maintenance. Most often, the role of collecting and disseminating statistical information regarding established problems is assumed by state agencies. In general, statistical claims about crime and other social problems reach the general public via the mass media - most importantly the news media. This paper examines news reporting about crime statistics which appeared in Canadian English language print media during the calendar years 1993 and 1994. A search of a computerized data base and a more detailed search of news items appearing during more intensive periods of statistical claim-making yielded a final sample of 244 news articles from major newspapers and newsmagazines. Two broad questions form the focus of the analysis. The first concerns the means by which statistical claims about crime and victimization enter the news flow. Put simply, to whose statistical claims about crime do journalists pay attention and what are the "news hooks" on which media discussions of rates, statistical trends and percentages are hung? The analysis finds that there are principally three routes by which crime statistics become news. The first and most common is the "data release" in the form of press conferences, the release of a new study or the regular release of data by state agencies. Not all of those who seek to make statistical claims of this type are equally likely to attract the attention of the media, and those agencies and individuals who occupy superordinate positions within a hierarchy of credibility are most likely to prove successful in this regard. Such credibility is most typically conveyed via the official status of the source. A second major form of news hook involves efforts at "debunking" or charges of statistical error. In these cases, "new" statistical findings call into question what earlier statistics encouraged audiences to believe. A third route by which crime and victimization statistics enter the news flow relates to the use of statistics as "background" information with respect to some more substantive theme. The second major question on which the paper is focused involves a consideration of the ways in which statistical news is packaged so as to ensure conformity with dominant news values. The analysis suggests that journalists employ a number of strategies to meet these objectives. Most important, there is considerable journalistic effort to dress stories involving statistics in ways that emphasize humour, drama and public interest. As well, an attempt is routinely made to emphasize the importance of reported statistics and the objective character of the reporting itself. A persistent criticism of media reporting of crime is that there is a clear journalistic preference for bad news. This analysis reveals, however, that journalists might be more interested in easy news than in bad news. The availability of official statistics (which in this sample, at least, often described stable or declining trends) and the reliance on liberal social scientists as a counterpoint to the more conservative voices of policing agencies and victims' organizations implies that the statistical images in the media are often more complex than they are assumed to be.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 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) | 0,007 | 0,000 |
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 ».