The four Visegrad states — Poland, the Czech Republic, Slovakia (until 1993 Czechoslovakia) and Hungary — form a compact area between Germany and Austria in the west and the states of the former USSR in the east. They are bounded by the Baltic in the north and the Danube river in the south. They are cut by the Sudeten and Carpathian mountain ranges, which divide Poland off from the other states. Poland is an extension of the North European plain and like the latter is drained by rivers that flow from south to north west — the Oder, the Vlatava and the Elbe, the Vistula and the Bug. The Danube is the great exception, flowing from its source eastward, turning through two 90-degree turns to end up in the Black Sea, forming the barrier and often the political frontier between central Europe and the Balkans. Hungary to the east of the Danube is also an open plain. The region is historically and culturally part of western Europe, but its eastern Marches now represents a vital strategic zone between Germany and the core of the European Union to the west and the Russian zone to the east.

Economy and Society

This 1966 paperback edition of a publication which first appeared in 1963 has by now been widely reviewed as a worthy contribution to the sociological study of deviant behavior. Its current appearance as a paperback is a testimonial both to the quality of the work and to the prominence of deviant behavior in this generation. In general the author places deviance in perspective, identifies types of deviant behavior, considers the role of rule makers and enforcers, and some of the problems in studying deviance. In addition, he develops a sequential model of deviance relying on the concept of career, a concept originally developed in studies of occupations. In his study of a particular kind of deviance, the use of marihuana, the author posits and tests systematically an hypothesis about the genesis of marihuana use for pleasure. The hypothesis traces the sequence of changes in individual attitude

Outsiders Studies in the Sociology of Deviance.

Nos últimos trinta trinta anos, houve profundas mudanças na forma como compreendemos o crime e a justiça criminal. O crime tornou-se um evento simbólico, um verdadeiro teste para a ordem social e para as políticas governamentais, um desafio para a sociedade civil, para a democracia e para os direitos humanos. Segundo David Garland, professor da Faculdade de Direito da New York University, um dos principais autores no campo da Sociologia da Punição e com artigo publicado na Revista de Sociologia e Política , número 13, na modernidade tardia houve uma verdadeira obsessão securitária, direcionando as políticas criminais para um maior rigor em relação às penas e maior intolerância com o criminoso. Há trinta anos, nos EUA e na Inglaterra essa tendência era insuspeita. O livro mostra que os dois países compartilham intrigantes similaridades em suas práticas criminais, a despeito da divisão racial, das desigualdades econômicas e da letalidade violenta que marcam fortemente o cenário americano. Segundo David Garland, encontram-se nos dois países os “mesmos tipos de riscos e inseguranças, a mesma percepção a respeito dos problemas de um controle social não-efetivo, as mesmas críticas da justiça criminal tradicional, e as mesmas ansiedades recorrentes sobre mudança e ordem sociais”1 (GARLAND, 2001, p. 2). O argumento principal da obra é o seguinte: a modernidade tardia, esse distintivo padrão de relações sociais, econômicas e culturais, trouxe consigo um conjunto de riscos, inseguranças e problemas de controle social que deram uma configuração específica às nossas respostas ao crime, ao garantir os altos custos das políticas criminais, o grau máximo de duração das penas e a excessivas taxas de encarceramento.

http://www.scielo.br/pdf/rsocp/n20/n20a15.pdf

The culture of control: crime and social order in contemporary, society

What makes organizations so similar? We contend that the engine of rationalization and bureaucratization has moved from the competitive marketplace to the state and the professions. Once a set of organizations emerges as a field, a paradox arises: rational actors make their organizations increasingly similar as they try to change them. We describe three isomorphic processes-coercive, mimetic, and normative—leading to this outcome. We then specify hypotheses about the impact of resource centralization and dependency, goal ambiguity and technical uncertainty, and professionalization and structuration on isomorphic change. Finally, we suggest implications for theories of organizations and social change.

The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields (Chinese Translation)

This is a study in industrial sociology; it a partial report, to be followed by another, of an investigation of singel factory seen in the light of Max Weber's theory of bureacracy.

Patterns of Industrial Bureaucracy.

This article examines the intersection of two structural developments: the growth of surveillance and the rise of “big data.” Drawing on observations and interviews conducted within the Los Angeles...

Big Data Surveillance: The Case of Policing:

The degree and scope of criminal justice surveillance increased dramatically in the United States over the past four decades. Recent qualitative research suggests the rise in surveillance may be me...

https://journals.sagepub.com/doi/pdf/10.1177/0003122414530398

Surveillance and System Avoidance: Criminal Justice Contact and Institutional Attachment

The number of predictive technologies used in the U.S. criminal justice system is on the rise. Yet there is little research to date on the reception of algorithms in criminal justice institutions. We draw on ethnographic fieldwork conducted within a large urban police department and a midsized criminal court to assess the impact of predictive technologies at different stages of the criminal justice process. We first show that similar arguments are mobilized to justify the adoption of predictive algorithms in law enforcement and criminal courts. In both cases, algorithms are described as more objective and efficient than humans’ discretionary judgment. We then study how predictive algorithms are used, documenting similar processes of professional resistance among law enforcement and legal professionals. In both cases, resentment toward predictive algorithms is fueled by fears of deskilling and heightened managerial surveillance. Two practical strategies of resistance emerge: footdragging and data obfuscation. We conclude by discussing how predictive technologies do not replace, but rather displace discretion to less visible—and therefore less accountable— areas within organizations, a shift which has important implications for inequality and the administration of justice in the age of big data. K E Y W O R D S : algorithms; prediction; policing; criminal courts; ethnography. In recent years, algorithms and artificial intelligence have attracted a great deal of scholarly and journalistic attention. Of particular interest is the development of predictive technologies designed to estimate the likelihood of a future event, such as the probability that an individual will default on a loan, the likelihood that a consumer will buy a specific product online, or the odds that a job candidate will have a long tenure in an organization. Predictive algorithms capture the imagination of scholars and journalists alike, in part because they raise the question of automated judgment: the replacement – or at least the augmentation – of human discretion by mechanical procedures. Nowhere are these The authors contributed equally and are listed alphabetically. We would like to thank the three anonymous reviewers, as well as the organizers and participants of the Willen Seminar at Barnard College in 2016, “Punishment, Society, and Technology” session of the LSA Annual Meeting in 2018, and “Innovations and Technology in Studies of Crime and Social Control” session of the ASA Annual Meeting in 2018 for their helpful comments and feedback. Please direct correspondence to Sarah Brayne at the Department of Sociology at the University of Texas at Austin, 305 E. 23rd Street, A1700, RLP 3.306, Austin, TX 78712; telephone (512) 475-8641; email sbrayne@utexas.edu. VC The Author(s) 2020. Published by Oxford University Press on behalf of the Society for the Study of Social Problems. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. 1 Social Problems, 2020, 0, 1–17 doi: 10.1093/socpro/spaa004 Article D ow naded rom http/academ ic.p.com /socpro/advance-articleoi/10.1093/socpro/spaa004/5782114 by Snford U niersity user on 06 M arch 2020 questions more salient than in the context of criminal justice. Over recent decades, the U.S. criminal justice system has witnessed a proliferation of algorithmic technologies. Police departments now increasingly rely on predictive software programs to target potential victims and offenders and predict when and where future crimes are likely to occur (Brayne 2017; Ferguson 2017). Likewise, criminal courts use multiple predictive instruments, called “risk-assessment tools,” to assess the risk of recidivism or failure to appear in court among defendants (Hannah-Moffat 2018; Harcourt 2006; Monahan and Skeem 2016). Predictive technologies, in turn, raise many questions about fairness and inequality in criminal justice. On the positive side, advocates emphasize the benefits of using “smart statistics” to reduce crime and improve a dysfunctional criminal justice system characterized by racial discrimination and mass incarceration (Brantingham, Valasik, and Mohler 2018; Milgram 2012). On the negative side, critics argue that algorithms tend to embed bias and reinforce social and racial inequalities, rather than reducing them (Benjamin 2019; Eubanks 2018; O’Neil 2016). They note that predictive algorithms draw on variables or proxies that are unfair and may be unconstitutional (Ferguson 2017; Starr 2014). Many point out that predictive algorithms may lead individuals to be surveilled and detained based on crimes they have not committed yet, frequently comparing these technologies to the science-fiction story Minority Report by Philip K. Dick and its movie adaptation, which evoke a dystopian future. To date, studies of criminal justice algorithms share three main characteristics. First, existing work tends to focus on the construction of algorithms, highlighting the proprietary aspect of most of these tools (which are often built by private companies) and criticizing their opacity (Angwin et al. 2016; Pasquale 2015; Wexler 2017). Second, they tend to treat the criminal justice system as a monolith, lumping together the cases of law enforcement, adjudication, sentencing, and community supervision (O’Neil 2016; Scannell 2016). Third, and most importantly, most studies fail to analyze contexts of reception, implicitly assuming – usually without empirical data – that police officers, judges, and prosecutors rely uncritically on what algorithms direct them to do in their daily routines (Harcourt 2006; Hvistendahl 2016; Mohler et al. 2015; Uchida and Swatt 2013). In this article, we adopt a different perspective. Building on a growing body of literature that analyzes the impact of big data in criminal justice (Hannah-Moffat, Maurutto, and Turnbull 2009; Lageson 2017; Lum, Koper, and Willis 2017; Sanders, Weston, and Schott 2015; Stevenson and Doleac 2018), as well as existing ethnographic work on the uses of algorithms (Brayne 2017; Christin 2017; Levy 2015; Rosenblat and Stark 2016; Shestakovsky 2017), we focus on the reception of predictive algorithms in different segments of the criminal justice system. Drawing on two indepth ethnographic studies – one conducted in a police department and the other in a criminal court – we examine two questions. First, to what extent does the adoption of predictive algorithms affect work practices in policing and criminal courts? Second, how do practitioners respond to algorithmic technologies (i.e., do they embrace or contest them)? Based on this ethnographic material, this article provides several key findings. First, we document a widespread – albeit uneven – use of big data technologies on the ground. In policing, big data are used for both person-based and place-based predictive identification, in addition to risk management, crime analysis, and investigations. In criminal courts, multiple predictive instruments, complemented by digital case management systems, are employed to quantify the risk of the defendants. Second, similar arguments are used in policing and courts to justify the use of predictive technologies. In both cases, algorithms are presented as more rational and objective than “gut feelings” or discretionary judgments. Third, we find similar strategies of resistance, fueled by fears of experiential devaluation and increased managerial surveillance, among law enforcement and legal professionals—most importantly, foot-dragging and data obfuscation. Despite these resemblances, we document important differences between our two cases. In particular, law enforcement officers were under more direct pressure to use the algorithms, whereas the legal professionals under consideration were able to keep their distance and ignore predictive technologies without consequences, a finding we relate to the 2 Brayne and Christin D ow naded rom http/academ ic.p.com /socpro/advance-articleoi/10.1093/socpro/spaa004/5782114 by Snford U niersity user on 06 M arch 2020 distinct hierarchical structures and levels of managerial oversight of the police department and criminal court we compared. We conclude by discussing the implications of these findings for research on technology and inequality in criminal justice. Whereas the current wave of critical scholarship on algorithmic bias often leans upon technological deterministic narratives in order to make social justice claims, here we focus on the social and institutional contexts within which such predictive systems are deployed and negotiated. In the process, we show that these tools acquire political nuance and meaning through practice, which can lead to unanticipated or undesirable outcomes: forms of workplace surveillance and the displacement of discretion to less accountable places. We argue that this sheds new light on the transformations of police and judicial discretion – with important consequences for social and racial inequality – in the age of big data. D E C I S I O N M A K I N G A C R O S S A V A R I E T Y O F D O M A I N S As a growing number of daily activities now take place online, an unprecedented amount of digital information is being collected, stored, and analyzed, making it possible to aggregate data across previously separate institutional settings. Harnessing this rapidly expanding corpus of digitized information, algorithms – broadly defined here as “[a] formally specified sequence(s) of logical operations that provides step-by-step instructions for computers to act on data and thus automate decisions” (Barocas et al. 2014) – are being used to guide decision-making across institutional domains as varied as education, journalism, credit, and criminal justice (Brayne 2017; Christin 2018; Fourcade and Healy 2017; O’Neil 2016; Pasquale 2015). Advocates for algorithmic technologies argue that by relying on “unbiased” assessments, algorithms may help deploy resources more efficiently and objective

Technologies of Crime Prediction: The Reception of Algorithms in Policing and Criminal Courts

The scope of criminal justice surveillance, from policing to incarceration, has expanded rapidly in recent decades. At the same time, the use of big data has spread across a range of fields, including finance, politics, health, and marketing. While law enforcement’s use of big data is hotly contested, very little is known about how the police actually use it in daily operations and with what consequences. This book offers an inside look at how police use big data and new surveillance technologies, leveraging on-the-ground fieldwork with one of the most technologically advanced law enforcement agencies in the world—the Los Angeles Police Department. Drawing on original interviews and ethnographic observations from over two years of fieldwork with the LAPD, the text examines the causes and consequences of big data and algorithmic control. It reveals how the police use predictive analytics and new surveillance technologies to deploy resources, identify criminal suspects, and conduct investigations; how the adoption of big data analytics transforms police organizational practices; and how the police themselves respond to these new data-driven practices. While big data analytics has the potential to reduce bias, increase efficiency, and improve prediction accuracy, the book argues that it also reproduces and deepens existing patterns of inequality, threatens privacy, and challenges civil liberties.

Predict and Surveil: Data, Discretion, and the Future of Policing

.............................................................................................iii Acknowledgements ..................................................................................iv List of Tables .......................................................................................viii List of Figures .......................................................................................ix Chapter

Sarah Brayne

Papers

Big Data Surveillance: The Case of Policing:

Surveillance and System Avoidance: Criminal Justice Contact and Institutional Attachment

Technologies of Crime Prediction: The Reception of Algorithms in Policing and Criminal Courts

Predict and Surveil: Data, Discretion, and the Future of Policing

Stratified Surveillance: Policing in the Age of Big Data