Local law enforcement terrorism prevention efforts: A state level case study

Although law enforcement agencies across the nation have assumed part of the responsibility in the fight against terrorism, significant confusion exists as to the role of local law enforcement. While some agencies have taken steps towards terrorism prevention, little data has been collected on which agencies have taken the greatest steps, how those agencies have changed, and how other agencies should direct their activity. This case study of law enforcement census data from a single state considered variations in terrorism preparedness and predictors of preparedness. Agency size, presence of funding, and other variables significantly predict levels of terrorism preparedness. Important policy implications such as the distribution of funding and which agencies should be working towards terrorism preparedness are discussed.     

Distrust of Government, the Vigilante Tradition, and Support for Capital Punishment

This study probes the interconnections among distrust of government, the historical context, and public support for the death penalty in the United States with survey data for area-identified samples of white and black respondents. Multilevel statistical analyses indicate contrary effects of government distrust on support for the death penalty for blacks and whites, fostering death penalty support among whites and diminishing it among blacks. In addition, we find that the presence of a "vigilante tradition," as indicated by a history of lynching, promotes death penalty support among whites but not blacks. Finally, contrary to Zimring's argument in The Contradictions of Capital Punishment , we find no evidence that vigilantism moderates the influence of government distrust on support for the death penalty, for either whites or blacks. Our analyses highlight the continuing influence of historical context as well as contemporary conditions in the formation of public attitudes toward criminal punishment, and they underscore the importance of attending to racial differences in the analysis of punitive attitudes.     

A Test of Generalizability of the Social Development Model Across Gender and Income Groups with Longitudinal Data from the Elementary School Developmental Period

The social development model (SDM) is a theory of behavior that has proven useful in explaining the etiology of delinquency, violence, and substance use among adolescents as well as early antisocial behavior among pre-adolescents. A further test of the model is its generalizability across population groups. A section of the SDM representing prosocial influences in the etiology of problem behavior was compared for boys and girls and for children from low- and non low-income families using three waves of child, parent and teacher survey data on a sample of 851 elementary school students. Multiple group structural equation modeling was used to assess differences across groups in both measurement of model constructs and hypothesized structural paths between constructs. The results indicate overall similarity in the reliability of measurement models and validity of structural models.     

Inmates: Who occupies our jails?

The purpose of this investigation was to identify inmate personal and demographic data that may be used to further define or profile persons with a greater likelihood of being at risk for serving a jail sentence. This study was explorative. The survey administration was similar in format to that conducted by the U.S. Department of Justice. Chaplains were used as survey proctors. Pilot studies were conducted to determine the suitability of survey content. Surveys consisted of 172 items. Subjects included four hundred inmates: males comprised 88.75 percent of the sample, Caucasians 58.25 percent, Blacks 37 percent, Hispanics 3.25 percent and other 1.5 percent of the sample. Respondents ranged in age from 15 to 64 years with an average age of 30.3 years. Less than two percent of the contacted inmates refused participation in the survey. Results are presented for demographic data, select legal/criminal data, select drug and alcohol use data, employment data, health, parent/family data, education data, social data, and “other”.