Measuring Procedural Justice Policy Adherence During Use of Force Events: The Body-Worn Camera as a Performance Monitoring Tool

This study capitalizes on a successful researcher–practitioner partnership to conduct a systematic social observation (SSO) of police body-worn camera (BWC) footage in Newark, NJ. To demonstrate the utility of BWCs as performance monitoring tools, we measure officer adherence to procedural justice standards throughout use of force events as mandated in the Newark Police Division’s updated policies pursuant to an ongoing federal consent decree. Overall, a slim majority of use of force events are procedurally just. However, results indicate several instances of policy noncompliance. Results are discussed, and policy recommendations related to procedural justice policy violations and BWCs for performance monitoring are provided.

Is There a Civilizing Effect on Citizens? Testing the Pre-Conditions for Body Worn Camera-Induced Behavior Change

The cause(s) of reduced use of force and complaints following police body-worn camera (BWC) deployment remain unclear, though some argue that BWCs generate a civilizing effect on citizen behavior. This potential effect rests on four pre-conditions: (1) BWC presence and citizen awareness; (2) BWC activation; (3) Escalated citizen behavior or the potential for escalation; (4) Citizen mental capacity for BWC awareness. Prior research has not established the civilizing effect’s existence, or how often these pre-conditions are met; this study aims to fill that gap. Data was collected during systematic social observation (SSO) of 166 encounters between citizens and officers in the Tempe, Arizona Police Department. The results tell a simple story. Two pre-conditions (activation, citizen mental capacity) are consistently met; awareness and escalated behavior are not. Overall, 1.2% of encounters saw all pre-conditions met. The paper concludes with a discussion of the implications for research on BWCs.

Understanding the Predictors of Street Robbery Hot Spots: A Matched Pairs Analysis and Systematic Social Observation

This study examines the environmental predictors that classify street robbery hot spots and control street segments in Indianapolis. Empirical controls were generated by matching each hot spot to a corresponding set of zero-crime control and low-crime control units. Then, units were evaluated based on the presence of crime generators and attractors, which were downloaded from open data sources and spatially joined to the street segments, and disorder indicators obtained via systematic social observation using Google Street View. The findings provide information about the influence environmental predictors have on the presence of street robbery hot spots, and whether the composition of hot spots significantly differs from that of similar places that experienced no crime or low counts of crime.