Breaking News : The Portrayal of Crime, Justice and Victimization on Broadcast News
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 0.000 |
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".