Document Title: Classifying Adult Probationers by Forecasting Future Offending

By G. Barnes, Jordan M. Hyatt. 2012

34 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. G. Duwe
2. Jordan M. Hyatt
3. KiDeuk Kim
4. G. Barnes
5. Richard A. Berk
6. Adam Lankford

Citations in the corpus

• Sacrificing Accuracy for Transparency in Recidivism Risk Assessment: The Impact of Classification Method on Predictive Performance (2016, methodology): “The ever-growing volume of administrative data has also given rise to the use of machine learning algorithms in a data-driven approach to risk prediction (Barnes & Hyatt, 2012; Berk, Sherman, Barnes, Kurtz, & Ahlman, 2009).”
• An Experimental Evaluation of the Impact of Intensive Supervision on the Recidivism of High-Risk Probationers (2017, methodology): “A long-term follow-up of this model’s forecasts (Barnes & Hyatt, 2012) showed that those placed into the high and moderate risk groups reoffended at very similar rates when crimes of any type were considered (54.8% of highs and 52.1% of moderates committed a new offense within 2 years).”