The Mathematics of Risk Classification: Changing Data into Valid Instruments for Juvenile Courts. NCJ 209158.
By D. Gottfredson, H. Snyder. 2005
46 citations recorded by the source corpus.
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Citations in the corpus
- Interpretable classification models for recidivism prediction (2015, methodology): “The majority of recidivism risk assessment tools were produced using logistic regression and substantially modified for the purposes of interpretability (see, e.g., the recommendations of Gottfredson and Snyder 2005).”
- Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice (2018, methodology): “We should not be using rounding heuristics if we want a reliable high quality solution, despite the government’s recommendation (21) to round logistic regression coefficients.”
- Reconciling the Differences Between the “Gender-Responsive” and the “What Works” Literatures to Improve Services for Girls (2008, background): “This, and the fact that the regression models used in these studies explain very little of the variation in recidivism for boys or girls (see Gottfredson & Snyder, 2005), begs the question What other factors underlie delinquent behavior?”
- The Meta-Analysis of Clinical Judgment Project: Fifty-Six Years of Accumulated Research on Clinical Versus Statistical Prediction (2006, background): “In fact, numerous funded research projects have been conducted to aid in classifying juvenile and adult prison inmates (e.g., Gottfredson & Snyder, 2005; Quinsey, Harris, Rice, & Cormier, 1998; Steadman et al., 2000; Sullivan, Cirincione, Nelson, & Wallis, 2001).”
- A Comparison of Risk Assessment Instruments in Juvenile Justice (2013, background): “Other distinguished researchers rely completely on measures other than the AUC in their studies of risk instruments (for example, Gottfredson & Snyder, 2005; Altman & Royston, 2000).”