Drug Traffickers in the Maryland Circuit Court: Their Past, Their Sentences, and Prediction and Prevention of Future Violence

Jinney Smith, Ph.D., Department of Criminology and Criminal Justice
Kiminori Nakumura, Ph.D., Department of Criminology and Criminal Justice

This research examines the risk of violent recidivism, focusing on offenders of drug offenses (particularly drug trafficking) in Maryland Circuit Courts. Using three different data sources, researchers profile drug traffickers’ juvenile and criminal history and predict their risk of violent recidivism by incorporating predictors such as age at first arrest/conviction, prior violent and weapon charges, and other variables. This research also hopes to display patterns of offending and criminal history that leads to current drug offense sentences and asks whether sentences for drug traffickers are proportional to the violent recidivism risk they pose and whether or not they are effective in preventing violent recidivism.

Recent criminal justice reform efforts characterize drug offenders as non-violence offenders who warrant reduced sentences but relying on the instant offense to determine which offenders are a low public safety risk could be misleading. Though drug dealers and traffickers are targeted for punitive sanctions (i.e. mandatory minimums and sentence enhancements), little is known empirically about their future propensity for violence.

Individual sentencing data from the State Commission on Criminal Sentencing (MSCCSP), Criminal Justice Information System (CJIS), Department of Public Safety and Correctional Services (DPSCS) and the Department of Juvenile Services (DJS) are merged to identify patterns of criminal history among drug offenders and drug traffickers. To date, researchers have identified factors which may help differentiate subsets of drug offenders with a particularly heightened risk of violent recidivism, including violent crime, and are completing the data cleaning and merging. Once the final dataset is completed, the researchers will examine descriptive statistics and use a regression model to assess drug offenders and their criminal histories.