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Abstract
We examine the factors influencing police response times, with a particular focus on staffing levels, calls for service (CFS), and proactive police work. We estimate Bayesian Holt-Winters state-space models for each CFS priority level. Using a novel dataset that combines data from the Salt Lake City Police Department's staffing and Computer-Aided Dispatch (CAD) systems at the daily level over seven years, we estimate the effects that staffing, overtime, call volume, and the level of proactive work (e.g., traffic stops, pedestrian stops, business checks) have on police response times. Our findings indicate that the impact of staffing on response times is significantly greater than that of other independent variables in the models. Furthermore, improvements in response times for higher-priority (i.e., more serious) CFS have a lower elasticity response to increases in staffing levels. As police agencies face increasingly complex challenges, the empirical evidence presented herein serves as a cornerstone for making informed decisions in the intricate balancing act of resources, officer well-being, and public safety priorities.

AbstractAdministrative discretion can range from benign to troubling, and law enforcement officers possess the power to use physical violence in the discharge of their duties. Body‐worn cameras (BWCs) are a workplace surveillance technology intended to monitor officer behavior in the field, but officers exercise discretion over whether or not to activate their cameras. So, what drives officers to activate BWCs? Combining unique survey and administrative data, three competing explanations of BWC activation are compared in one department: Officer demographics, job function, and attitudes. Job function covariates offer robust predictive power of BWC activation frequency. Demographics do not predict BWC activations except rank, which negatively correlates with activation. Though the bulk of attitudinal measures do not predict BWC activations, negative relationships are noted with how officers perceive BWCs to impact their professional discretion, and their belief that cameras expose officers to public outrage and disapproval.

AbstractAmong the more recognizable programs related to natural and sustainable food is the United States Department of Agriculture's National Organic Program. Although the robustness of the organic food market is difficult to contest, many debate the extent to which U.S. organic policy outcomes adequately serve consumers and the organic agriculture producers they rely on. This paper engages the debate from the perspective of certified organic producers. Drawing on the results of a nationwide survey of USDA‐certified producers, we first provide a snapshot of how producers assess the environmental, consumer, and market impacts of U.S. organic food policy. We then examine the extent to which organic producers' policy impact perceptions are associated with their alignment with an "organic ethos"—understood as producers' commitment to core organic principles and the organic movement. The paper highlights producers' values as perceptual filters and cognitive mechanisms that help shape producers' policy impacts perceptions, illustrating a contributing factor to the enduring nature of organic policy debates.

AbstractLaw enforcement agencies are increasingly adopting artificial intelligence (AI)‐powered tools. While prior work emphasizes the technological features driving public opinion, we investigate how public trust and support for AI in government vary with the institutional context. We administer a pre‐registered survey experiment to 4200 respondents about AI use cases in policing to measure responsiveness to three key institutional factors: bureaucratic proximity (i.e., local sheriff versus national Federal Bureau of Investigation), algorithmic targets (i.e., public targets via predictive policing versus detecting officer misconduct through automated case review), and agency capacity (i.e., necessary resources and expertise). We find that the public clearly prefers local over national law enforcement use of AI, while reactions to different algorithmic targets are more limited and politicized. However, we find no responsiveness to agency capacity or lack thereof. The findings suggest the need for greater scholarly, practitioner, and public attention to organizational, not only technical, prerequisites for successful government implementation of AI.