RT Article
T1 Predicting sentencing outcomes with centrality measures
A1 Masías Hinojosa, Víctor Hugo 1979-
A2 Morselli, Carlo
A2 Crespo, Fernando
A2 Laengle, Sigifredo
LA English
YR 2013
UL https://krimdok.uni-tuebingen.de/Record/1588524566
AB 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
CN 340 301
K1 Kriminologie
K1 Drogenkriminalität
K1 Soziales Netzwerk
K1 Strafverfolgung
K1 Justiz
K1 Netzwerkanalyse
K1 Criminology
K1 Sentencing outcomes
K1 Social Networks
DO 10.1186/2190-8532-2-4