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      Procedural justice training reduces police use of force and complaints against officers

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          Significance

          Police misconduct and use of force have come under increasing scrutiny and public attention. The procedural justice model of policing, which emphasizes transparency, explaining policing actions, and responding to community concerns, has been identified as a strategy for decreasing the number of interactions in which civilians experience disrespectful treatment or the unjustified use of force. This paper evaluates whether a large-scale implementation of procedural justice training in the Chicago Police Department reduced complaints against police and the use of force against civilians. By showing that training reduced complaints and the use of force, this research indicates that officer retraining in procedural justice is a viable strategy for decreasing harmful policing practices and building popular legitimacy.

          Abstract

          Existing research shows that distrust of the police is widespread and consequential for public safety. However, there is a shortage of interventions that demonstrably reduce negative police interactions with the communities they serve. A training program in Chicago attempted to encourage 8,480 officers to adopt procedural justice policing strategies. These strategies emphasize respect, neutrality, and transparency in the exercise of authority, while providing opportunities for civilians to explain their side of events. We find that training reduced complaints against the police by 10.0% and reduced the use of force against civilians by 6.4% over 2 y. These findings affirm the feasibility of changing the command and control style of policing which has been associated with popular distrust and the use of force, through a broad training program built around the concept of procedurally just policing.

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          Most cited references20

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          Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models

          Yiqing Xu (2017)
          Difference-in-differences (DID) is commonly used for causal inference in time-series cross-sectional data. It requires the assumption that the average outcomes of treated and control units would have followed parallel paths in the absence of treatment. In this paper, we propose a method that not only relaxes this often-violated assumption, but also unifies the synthetic control method (Abadie, Diamond, and Hainmueller 2010) with linear fixed effects models under a simple framework, of which DID is a special case. It imputes counterfactuals for each treated unit using control group information based on a linear interactive fixed effects model that incorporates unit-specific intercepts interacted with time-varying coefficients. This method has several advantages. First, it allows the treatment to be correlated with unobserved unit and time heterogeneities under reasonable modeling assumptions. Second, it generalizes the synthetic control method to the case of multiple treated units and variable treatment periods, and improves efficiency and interpretability. Third, with a built-in cross-validation procedure, it avoids specification searches and thus is easy to implement. An empirical example of Election Day Registration and voter turnout in the United States is provided.
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            Language from police body camera footage shows racial disparities in officer respect

            Police officers speak significantly less respectfully to black than to white community members in everyday traffic stops, even after controlling for officer race, infraction severity, stop location, and stop outcome. This paper presents a systematic analysis of officer body-worn camera footage, using computational linguistic techniques to automatically measure the respect level that officers display to community members. This work demonstrates that body camera footage can be used as a rich source of data rather than merely archival evidence, and paves the way for developing powerful language-based tools for studying and potentially improving police–community relations. Using footage from body-worn cameras, we analyze the respectfulness of police officer language toward white and black community members during routine traffic stops. We develop computational linguistic methods that extract levels of respect automatically from transcripts, informed by a thin-slicing study of participant ratings of officer utterances. We find that officers speak with consistently less respect toward black versus white community members, even after controlling for the race of the officer, the severity of the infraction, the location of the stop, and the outcome of the stop. Such disparities in common, everyday interactions between police and the communities they serve have important implications for procedural justice and the building of police–community trust.
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              Regional Policy Evaluation: Interactive Fixed Effects and Synthetic Controls

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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc. Natl. Acad. Sci. U.S.A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                5 May 2020
                20 April 2020
                20 April 2020
                : 117
                : 18
                : 9815-9821
                Affiliations
                [1] aInstitute for Policy Research, Northwestern University , Evanston, IL 60208;
                [2] bYale Law School, Yale University , New Haven, CT 06511;
                [3] cDepartment of Sociology, Northwestern University , Evanston, IL 60208
                Author notes
                1To whom correspondence may be addressed. Email: george.wood@ 123456northwestern.edu .

                Edited by Susan T. Fiske, Princeton University, Princeton, NJ, and approved March 20, 2020 (received for review December 6, 2019)

                Author contributions: G.W., T.R.T., and A.V.P. designed research; G.W. performed research; G.W. analyzed data; and G.W., T.R.T., and A.V.P. wrote the paper.

                Author information
                http://orcid.org/0000-0003-0046-4465
                http://orcid.org/0000-0002-2647-7670
                Article
                201920671
                10.1073/pnas.1920671117
                7211954
                32312803
                f259c5fc-4831-4fb8-8d4b-b3dee20d3b18
                Copyright © 2020 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                Page count
                Pages: 7
                Categories
                Social Sciences
                Social Sciences

                procedural justice,policing,misconduct,complaints,force
                procedural justice, policing, misconduct, complaints, force

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