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      Ethnic Bias in Judicial Decision Making: Evidence from Criminal Appeals in Kenya

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          Abstract

          Understanding sources of judicial bias is essential for establishing due process. To date, theories of judicial decision making are rooted in ranked societies with majority–minority group cleavages, leaving unanswered which groups are more prone to express bias and whether it is motivated by in-group favoritism or out-group hostility. We examine judicial bias in Kenya, a diverse society that features a more complex ethnic landscape. While research in comparative and African politics emphasizes instrumental motivations underpinning ethnic identity, we examine the psychological, implicit biases driving judicial outcomes. Using data from Kenyan criminal appeals and the conditional random assignment of judges to cases, we show that judges are 3 to 5 percentage points more likely to grant coethnic appeals than non-coethnic appeals. To understand mechanisms, we use word embeddings to analyze the sentiment of written judgments. Judges use more trust-related terms writing for coethnics, suggesting that in-group favoritism motivates coethnic bias in this context.

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          Most cited references101

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          The Psychology of Prejudice: Ingroup Love and Outgroup Hate?

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            Intergroup bias.

            This chapter reviews the extensive literature on bias in favor of in-groups at the expense of out-groups. We focus on five issues and identify areas for future research: (a) measurement and conceptual issues (especially in-group favoritism vs. out-group derogation, and explicit vs. implicit measures of bias); (b) modern theories of bias highlighting motivational explanations (social identity, optimal distinctiveness, uncertainty reduction, social dominance, terror management); (c) key moderators of bias, especially those that exacerbate bias (identification, group size, status and power, threat, positive-negative asymmetry, personality and individual differences); (d) reduction of bias (individual vs. intergroup approaches, especially models of social categorization); and (e) the link between intergroup bias and more corrosive forms of social hostility.
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              Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts

              Politics and political conflict often occur in the written and spoken word. Scholars have long recognized this, but the massive costs of analyzing even moderately sized collections of texts have hindered their use in political science research. Here lies the promise of automated text analysis: it substantially reduces the costs of analyzing large collections of text. We provide a guide to this exciting new area of research and show how, in many instances, the methods have already obtained part of their promise. But there are pitfalls to using automated methods—they are no substitute for careful thought and close reading and require extensive and problem-specific validation. We survey a wide range of new methods, provide guidance on how to validate the output of the models, and clarify misconceptions and errors in the literature. To conclude, we argue that for automated text methods to become a standard tool for political scientists, methodologists must contribute new methods and new methods of validation.
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                Author and article information

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                Journal
                American Political Science Review
                Am Polit Sci Rev
                Cambridge University Press (CUP)
                0003-0554
                1537-5943
                February 10 2022
                : 1-14
                Article
                10.1017/S000305542100143X
                aa232665-ee9c-4ae4-9ded-56b760ffb1d5
                © 2022

                https://creativecommons.org/licenses/by-nc-nd/4.0/

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