This report was submitted by Team Aurors as a participant in the U.S. Justice Department’s National Institute of Justice’s (NIJ’s) Recidivism Forecasting Challenge, which aimed to improve the ability to forecast recidivism.
The work reported by the Aurors Team addresses forecasting models that include regression analysis methods (binary logit and LASSO regressions) and machine learning techniques (random forest), combined through a model averaging procedure. The output consists of the percent likelihood of individuals recidivating within one, two, or three years from release. Although this report explains the database construction process and the modeling approach, the results focus on the section of female parolees recidivating within 3 years, since that is the category for which the Aurors team came in second place.
- One-Year Outcomes of a Drug Abuse Prevention Program for Older Teens and Emerging Adults: Evaluating a Motivational Interviewing Booster Component
- Drug Prevention in a Community Setting: A Longitudinal Study of the Relative Effectiveness of a 3-Year Primary Prevention Program in Boys & Girls Clubs Across the Nation
- Offender Transfer Notification Service (OTNS) Specification, Version 1.0