Predicting recidivism in violent sex offenders

Sexual Offence Recidivism:

The accuracy, fairness, and limits of predicting recidivism | Science Advances

Please consult with a translator for accuracy if you are relying on the translation or are using this site for official business. If you have any questions please contact: Bilingual Services Program at About Megan's Law. About Sex Offenders. Search Offenders. Translate Website Traducir Sitio Web. Risk Assessment.

Original Research ARTICLE

StaticR Risk Scores The StaticR score is used to predict risk of sexual reoffense, based on the offender's score category. For more detailed information about sexual recidivism in California, see Hanson, R.

Trailer park entirely inhabited by paedophiles and sex offenders - 60 Minutes Australia

Individuals were deemed to have transitioned to a lower risk category when their time-adjusted risk for that year was below the yearly hazard at release for individuals at the top of the next lower category. The figure stops at StaticR scores of 10 because higher scores were rare: 0. See Hanson, R. California Sex Offender Name Search. Enter a name and press Search. Select One How to report registrant info Unable to locate a registrant on the Website How do risk scores calculate re-offense rates?

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Predictors of Sexual Recidivism: An Updated Meta-Analysis 2004-02

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This question engages a deeper question about prevention, not simply prediction. Loeber, R. This decision creates the world within which statistical models generate predictions. Get involved today, and stay connected for life. To address the threats identified in this Article, risk tools used for the administration of criminal justice must reflect the values of the communities where the tools are applied. Behavioral Sciences and the Law, 16 , 97— Criminal Justice and Behavior, 36, -

Table 2 Algorithmic predictions from defendants. Algorithmic assessment Our algorithmic analysis used the same seven features as described in the previous section extracted from the records in the Broward County database. Perry, B. McInnis, C. Price, S. Smith, J. Angwin, J. Larson, S. Mattu, L. Dieterich, C.

Prediction of the risk of male sexual reoffending in Australia

Mendoza, T. Flores , K. Bechtel , C. Kleinberg, S. Mullainathan, M. Corbett-Davies, E. Pierson, A. Feller, S. Angelino, N. Larus-Stone, D. Alabi, M. Seltzer, C. Hastie , T. Kameda , The robust beauty of majority rules in group decisions. Gendreau , T. Little , C. Goggin , A meta-analysis of the predictors of adult offender recidivism: What works! Criminology 34 , — Hanson , M. Hanson , K.

Morton-Bourgon , The accuracy of recidivism risk assessments for sexual offenders: A meta-analysis of prediction studies.

Hanson , A. Harris , Where should we intervene? Dynamic predictors of sexual offense recidivism. Justice Behav. Beech , C. Friendship , M. Erikson , R. Hanson , The relationship between static and dynamic risk factors and reconviction in a sample of U. Abuse 14 , — Zeng , B.

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Ustun , C. Rudin , Interpretable classification models for recidivism prediction. A , — Geraghty , J. Woodhams , The predictive validity of risk assessment tools for female offenders: A systematic review. Violent Behav. Yang , S. Wong , J. Coid , The efficacy of violence prediction: A meta-analytic comparison of nine risk assessment tools.

Cox , The regression analysis of binary sequences.

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