Is Everyone Rated Equal? An Examination of Factors Related to Sexual Risk in Ethnically Diverse Male Adolescents Who Have Sexually Offended

As the juvenile justice system has evolved, there has been a need for clinicians to make judgments about risk posed by adolescents who have committed sexual offenses. There are inherent difficulties in attempting to assess risk for violence among adolescents due to the developmental changes taking place and the absence of well-validated instruments to guide risk prediction judgments. With minority groups increasing in numbers in the U.S., it is likely that professionals will encounter minority individuals when conducting risk assessments. Overall questions regarding race/ethnicity have been neglected and there are few if any published research that explores risk factors with minority juvenile sex offenders. The present study examined whether differences exist between Caucasian and racial/ethnic minority adolescent sexual offenders on four risk assessment measures (J-SORRAT-II, J-SOAP-II, SAVRY, and ERASOR). The sample of 207 male adolescent sexual offenders was drawn from treatment facilities in a Midwestern state. Overall results indicated that minority adolescent sex offenders had fewer risk factors endorsed than Caucasian youth across all risk assessment tools. Exploration of interactions between race and factors such as: family status, exposure to family violence, and family history of criminality upon the assessment tools risk ratings yielded non-significant findings. Limitations, suggestions for future directions, and clinical implications are discussed.

Spatial modelling and prediction on river networks: up model, down model or hybrid?

Preservation of rivers and water resources is crucial in most environmental policies and many efforts are made to assess water quality. Environmental monitoring of large river networks are based on measurement stations. Compared to the total length of river networks, their number is often limited and there is a need to extend environmental variables that are measured locally to the whole river network. The objective of this paper is to propose several relevant geostatistical models for river modeling. These models use river distance and are based on two contrasting assumptions about dependency along a river network. Inference using maximum likelihood, model selection criterion and prediction by kriging are then developed. We illustrate our approach on two variables that differ by their distributional and spatial characteristics: summer water temperature and nitrate concentration. The data come from 141 to 187 monitoring stations in a network on a large river located in the Northeast of France that is more than 5000 km long and includes Meuse and Moselle basins. We first evaluated different spatial models and then gave prediction maps and error variance maps for the whole stream network.