Predicting sentencing outcomes with centrality measures
1 School of Criminology, Université de Montréal, C.P. 6128, succursale Centre-ville, Montreal, QC H3C-3 J7, Canada
2 Faculty of Economics and Business, Universidad Diego Portales, Manuel Rodríguez Sur 253, 8370057, Santiago de, Chile
3 Department of Management Control, University of Chile, Diagonal Paraguay 257, 8330015, Santiago de, Chile
4 Universidad de Valparaíso, Brigadier de La Cruz 1050, 8900183, Santiago de, Chile
Security Informatics 2013, 2:4 doi:10.1186/2190-8532-2-4Published: 22 January 2013
Despite their importance for stakeholders in the criminal justice system, few methods have been developed for determining which criminal behavior variables will produce accurate sentence predictions. Some approaches found in the literature resort to techniques based on indirect variables, but not on the social network behavior with exception of the work of Baker and Faulkner [ASR 58: 837–860, 1993]. Using information on the Caviar Network narcotics trafficking group as a real-world case, we attempt to explain sentencing outcomes employing the social network indicators. Specifically, we report the ability of centrality measures to predict a) the verdict (innocent or guilty) and b) the sentence length in years. We show that while the set of indicators described by Baker and Faulkner yields good predictions, introduction of the additional centrality measures generates better predictions. Some ideas for orienting future research on further improvements to sentencing outcome prediction are discussed.