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California's Inmate Classification System: Predicting Inmate Misconduct

University of California, Riverside

MAX NEIMAN

University of California, Riverside

The goal of this article is to provide insight on the predictive value of California's inmate classification system. The authors argue that the current system is based heavily on an inmate's sentence length and that such an indicator provides little or no predictive value for inmate misconduct and is therefore not meeting the policy objectives assigned to classification and risk assessment. The authors use a large data set (N = 13,000) based on incident reports from 1992 to 1994 to test the hypothesis that the length of an inmate sentence is positively related to the rate of serious misconduct. The findings show that an inmate's sentence length is not related to infraction rates during incarceration and that a multivariate analysis produces negative coefficients.

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