The Complexities of Prior Record, Race, Ethnicity, and Policy

Crow, Matthew S.

doi:10.1177/0734016808320709

Cited by 32 publications

(28 citation statements)

References 41 publications

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“…Criminal justice involvement earlier in life can have persistent impacts on one's penal career (Crow, 2008; Kutateladze, Andiloro, Johnson, & Spohn, 2014; Mueller‐Smith & Schnepel, 2019; Pager, 2003; Wooldredge, Frank, Goulette, & Travis, 2015). First, earlier convictions often become permanent records and accumulate into criminal history.…”

Section: Crime–punishment Wave and Cohort Effectsmentioning

confidence: 99%

“…Criminal history consistently and strongly predicts the severity of sanctions for a new crime later in the life (Kleck, 1981; Spohn, 2000; Steffensmeier, Ulmer, & Kramer, 1998). Police, prosecutors, or judges can react more punitively to offenders with prior convictions at each stage of a criminal proceeding (Crow, 2008; Kutateladze et al., 2014; Wooldredge et al., 2015). At the system level, “penal code escalations or risk classification algorithms” in the sentencing process often depend on criminal conviction history (Mueller‐Smith & Schnepel, 2019, p. 19).…”

Section: Crime–punishment Wave and Cohort Effectsmentioning

confidence: 99%

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Locking up my generation: Cohort differences in prison spells over the life course

et al. 2020

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Crime rates have dropped substantially in the United States, but incarceration rates have remained high. The standard explanation for the lasting trend in incarceration is that the policy choices from the 1980s and 1990s were part of a secular increase in punitiveness that has kept rates of incarceration high. Our study highlights a heretofore overlooked perspective: that the crime–punishment wave in the 1980s and 1990s created cohort differences in incarceration over the life course that changed the level of incarceration even decades after the wave. With individual‐level longitudinal sentencing data from 1972 to 2016 in North Carolina, we show that cohort effects—the lingering impacts of having reached young adulthood at particular times in the history of crime and punishment—are at least as large (and likely much larger) than annual variation in incarceration rates attributable to period‐specific events and proclivities. The birth cohorts that reach prime age of crime during the 1980s and 1990s crime–punishment wave have elevated rates of incarceration throughout their observed life course. The key mechanism for their elevated incarceration rates decades after the crime–punishment wave is the accumulation of extended criminal history under a sentencing structure that systematically escalates punishment for those with priors.

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Section: Crime–punishment Wave and Cohort Effectsmentioning

confidence: 99%

Section: Crime–punishment Wave and Cohort Effectsmentioning

confidence: 99%

Locking up my generation: Cohort differences in prison spells over the life course

et al. 2020

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“…The final set of input variables is representative of well-known risk factors for recidivism (Bushway and Piehl, 2007;Crow, 2008) and has been used in risk assessment tools since 1928 (see, for example, Borden (1928), Hinojosa et al (2005), Berk et al (2006) and Baradaran (2013)). They include (a) information about prison release in 1994 (e.g.…”

Section: Deriving Input Variablesmentioning

confidence: 99%

Interpretable Classification Models for Recidivism Prediction

Zeng

Ustun

Rudin

2016

Journal of the Royal Statistical Society Series A: Statistics in Society

187

143

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These authors contributed equally to this work. Summary.We investigate a long-debated question, which is how to create predictive models of recidivism that are sufficiently accurate, transparent, and interpretable to use for decision-making. This question is complicated as these models are used to support different decisions, from sentencing, to determining release on probation, to allocating preventative social services. Each case might have an objective other than classification accuracy, such as a desired true positive rate (TPR) or false positive rate (FPR). Each (TPR, FPR) pair is a point on the receiver operator characteristic (ROC) curve. We use popular machine learning methods to create models along the full ROC curve on a wide range of recidivism prediction problems. We show that many methods (SVM, SGB, Ridge Regression) produce equally accurate models along the full ROC curve. However, methods that designed for interpretability (CART, C5.0) cannot be tuned to produce models that are accurate and/or interpretable. To handle this shortcoming, we use a recent method called Supersparse Linear Integer Models (SLIM) to produce accurate, transparent, and interpretable scoring systems along the full ROC curve. These scoring systems can be used for decision-making for many different use cases, since they are just as accurate as the most powerful black-box machine learning models for many applications, but completely transparent, and highly interpretable.

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“…Our court observations suggested that these linkages are not intended to mitigate offender responsibility but to show how systemic factors embedded within shared Aboriginal history contribute to criminal records and over-incarceration; ideally, these linkages can help disrupt this cycle one case at a time. This process differs from traditional court practices, where factors such as a lengthy criminal history are generally used to demonstrate the “riskiness” of an offender and to justify detention or incarceration (Crow 2008; Roberts 1997). When an offender’s behaviour is located within the broader experiences of victimization and disadvantage, it becomes possible to consider the extent to which he or she has been placed at risk and victimized; this moves the analysis beyond a narrow focus on his or her level of risk or risk of recidivism.…”

Section: Reconstituting the Legal Subjectmentioning

confidence: 99%

Aboriginal Knowledges in Specialized Courts: Emerging Practices in Gladue Courts

Maurutto¹,

Hannah‐Moffat²

2016

Can. j. law soc.

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Reforms to Canadian sentencing law in 1996 and the Supreme Court of Canada decision R. v. Gladue [1999] opened the door to a new normative set of legal practices that endeavour to integrate racial knowledge about offenders' collective and individual experiences of race relations and oppression into traditional legal criminal practices. One outcome of the reforms and court cases was the formation of dedicated Gladue courts for Aboriginal peoples. This paper explores the formation of Gladue courts, the legal techniques used to produce contextualized racial knowledges, how this information is admitted as evidence before the court, and how this knowledge is used to reframe legal subjects and the risk they pose.Keywords: Gladue court, sentencing, risk, Indigenous, race relations Résumé Des réformes apportées en 1996 aux lois concernant la détermination de la peine, ainsi que la décision R. c. Gladue de 1999 de la Cour suprême du Canada, ont ouvert la voie à un nouvel ensemble de pratiques normatives visant à intégrer aux pratiques pénales traditionnelles les connaissances sur les expériences collectives et individuelles du contrevenant en matière de relations et d' oppression raciales. L'un des résultats de ces réformes et affaires a été la création de tribunaux Gladue spéciaux pour les personnes autochtones. Cet article examine la création des tribunaux Gladue, les techniques juridiques employées pour obtenir des connaissances raciales contextualisées, la façon par laquelle cette information est prise en compte par la Cour, et la façon par laquelle ces connaissances sont appliquées pour resituer les sujets juridiques et les risques qu'ils posent.

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The Complexities of Prior Record, Race, Ethnicity, and Policy

Cited by 32 publications

References 41 publications

Locking up my generation: Cohort differences in prison spells over the life course

Locking up my generation: Cohort differences in prison spells over the life course

Interpretable Classification Models for Recidivism Prediction

Aboriginal Knowledges in Specialized Courts: Emerging Practices in Gladue Courts

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