Breaking News : The Portrayal of Crime, Justice and Victimization on Broadcast News
Notice bibliographique
Résumé
The media is instrumental in the construction of criminality and criminal justice. This study systematically analyzed the presentation of crime within local television newscasts. Content analysis was employed to analyze four hundred news broadcasts across four television markets. Bivariate and multivariate techniques were employed to test differences between large/small market stories and Canadian/American newscasts. The relationship between story characteristics, type of crime and demographic characteristics (race, gender, age) of victims and suspects was examined. Compared to Canadian crime stories, American crime stories were more likely to be sensational, more likely to present female victims and more likely to report younger victims. Similarly, large market crime stories were more likely to report stories that present fear and sensationalism. Newscasts provide less coverage for minority victims and male victims. Crime stories that involve a proactive police response were more likely to involve a white victim. Stories that present sympathy and outrage towards victimization were less likely to involve a minority victim. Stories that appear in later stages of the criminal justice system and non-local crime stories were more likely to involve female victims. Legitimization is the most important aspect of victimization. A victim must be perceived as "innocent" to be deserving of coverage. The media promotes an idealized and unrealistic picture of the "typical" crime victim. Many crimes involving minority victims were excluded from this romanticized portrayal of the crime victim. Similarly, crime stories with a proactive police response and the suspect displayed in handcuffs were more likely to involve non-white suspects. Finally, crime stories that present fear and outrage were more likely to report younger suspects. Suggestions for news reporters include expanding sources beyond law enforcement, providing further context to crime stories, including more follow-up coverage of crime stories, and providing more positive stories about African Americans and ethnic minorities.
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 | 0,001 | 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) | 0,000 | 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 ».