Suchergebnisse

Chapter 1. Introduction to Policing in an Age of Reform -- Chapter 2. Changing the Game: A Sociological Perspective on Police Reform -- Chapter 3. Community Policing: Often Advocated, Rarely Practiced -- Chapter 4. A Global Overview of Policing in an Age of Reform -- Chapter 5. Community Dynamics, Collective Efficacy, and Police Reform -- Chapter 6. Policing Hate Crime: Dilemmas in Policy and Practice -- Chapter 7. Police Responses to Islamic Radicalisation and Violent Extremisms -- Chapter 8. Forensic Science Understanding by Police Managers: New Opportunities to Re-think Its Involvement in Policing -- Chapter 9. Managing Police Performance -- Chapter 10. Cooperation Between Police and Civil Society -- Chapter 11. Digital Transformation if Forensic Science and Intelligence -- Chapter 12. Tools for a New Situational Policing -- Chapter 13. Heaven or Hell: Social Media for Intelligence -- Chapter 14. Doing Right: Police Ethics in an Age of Reform -- Chapter 15. The Allegory of the Dangerous Intersection: A Structural View of Law Enforcement and Social Problems -- Chapter 16. Conclusions and Future Directions in Policing.

Over the past two decades, significant efforts have established categories of crimes motivated by bias and so enhanced the quality of information about the prevalence of such crimes in the United States. As part of a national reporting system established by the Hate Crime Statistics Act, local police agencies collect information about the prevalence and characteristics of bias-crime incidents. Although the quality of this program has improved since its inception, local police face several challenges to identifying and accurately classifying bias crimes, including the ambiguity of applying legal definitions to cases, uncertainty regarding bias motivation, and infrequency of reported events to law enforcement. Drawing on information from eight case studies, the article examines how local police identify and record bias crimes through various kinds of reporting procedures and organizational structures. The article concludes with best practice recommendations for bias-crime tracking and reporting of incidents of bias crime within local police agencies.

The current study introduces a method to assess hate crime classification error in a state Incident-Based Reporting System. The study identifies and quantifies the "statistical accuracy" of aggregate hate crime data and provides insight from frontline officers about thought processes involved with classifying bias offenses. Random samples of records from two city and two county agencies provided data for the study. A systematic review of official case narratives determined hate crime classification error using state and federal definitions. A focus group sought to inquire about officers' handling of hate crimes. Undercounting of hate crimes in official data was evident. When error rates were extrapolated, National Incident-Based Reporting System Group A hate crimes were undercounted by 67%. Officers' responses validated complications involved with classifying hate crimes, particularly, incidents motivated "in part" by bias. Classification errors in reporting hate crimes have an impact on the statistical accuracy of official hate crime statistics. Officers' offense descriptions provided greater awareness to issues with accurately interpreting and classifying hate crimes. The results yield useful information for officer training, understanding the true magnitude of these crimes, and a precursor for adjusting crime statistics to better estimate the "true" number of hate crimes in the population.