Title: Is criminogenic risk assessment a prisoner of the proximate? Challenging the assumptions of an expanding paradigm
Description: Criminogenic risk assessment, which was developed to predict recidivism, has risen to the status of “evidence-based practice” in corrections systems. As a result of its apparent success, proponents now claim that it captures the origins of criminal behavior, and can thus be leveraged to reduce correctional supervision rates and prevent crime. This dissertation investigates the validity of the these claims, by identifying and testing three assumptions requisite for the framework’s expansion: 1) the evidence base for the framework’s predictive performance is being interpreted correctly and appropriately, 2) the best causal models of recidivism are also the best causal models of the onset and duration of criminal behavior (and by extension, that interventions successful at reducing recidivism will be successful at reducing the onset, duration, and rate of criminal behavior); and 3) the causes of individual variation in criminal behavior are the same as causes of the population distribution, or incidence rate, of crime.

This dissertation proceeds in three parts: a meta-review and critical analysis of the literature addresses the first assumption, and two empirical studies test the second and third assumptions, respectively. The meta-review determined that findings for the framework’s predictive performance are inconsistent, based on inadequate or insufficient statistical information, and often overstated. The first empirical study found that each arrest, and to a lesser extent conviction, an individual experienced increased their subsequent criminogenic risk levels, raising concerns about the framework’s applicability for crime prevention and etiology. The second empirical study found that criminogenic risks do not explain group differences in arrest and conviction rates, underscoring that researchers and policymakers should more cautiously communicate the scope of reform that the framework can deliver.