2002/10/01 by Eric Silver, Lynette Chow-Martin · 1 citation
Social Sciences · Economics, Econometrics and Finance · Engineering · Psychology · Medicine · #Crime Patterns and Interventions #Law, Economics, and Judicial Systems #Criminal Justice and Corrections Analysis #Recidivism #Context (archaeology) #USable #Risk assessment #Actuarial science #Computer science #Set (abstract data type) #Risk analysis (engineering) #Poison control #Engineering #Psychology #Computer security #Medicine #Environmental health #Criminology #Business
paper · doi:10.1177/009385402236732
openalex publication_date 2002/10/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/06/26
This study used a large recidivism data set to develop and validate a multiple models tool for predicting recidivism risk. Consistent with prior research, the authors found that the multiple models tool was more accurate than tools built using the traditional single-model approach. In addition, they demonstrated that the predicted recidivism rates produced by the multiple models tool could be summarized in a usable format consisting of four to five statistically distinct risk classes offering an impressive degree of base-rate dispersion. Given that public protection ranks as a primary focal concern of judges, the authors believe that their results justify renewed attention to the potential uses of actuarial tools within the context of judicial decision making.