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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".