Validation of Tool Mark Comparisons Obtained Using a Quantitative,Comparative, Statistical Algorithm

Abstract: A statistical analysis and computational algorithm for comparing pairs of tool marks via profilometry data is described. Empirical validation of the method is established through experiments based on tool marks made at selected fixed angles from 50 sequentially manufactured screwdriver tips. Results obtained from three different comparison scenarios are presented and are in agreement with experiential knowledge possessed by practicing examiners. Further comparisons between scores produced by the algorithm and visual assessments of the same tool mark pairs by professional tool mark examiners in a blind study in general show good agreement between the algorithm and human experts. In specific instances where the algorithm had difficulty in assessing a particular comparison pair, results obtained during the collaborative study with professional examiners suggest ways in which algorithm performance may be improved. It is concluded that the addition of contextual information when inputting data into the algorithm should result in better performance.     

The use of error rates to predict and characterize the efficacy of presumptive field tests

From a forensic perspective, a presumptive test, one which indicates the presence or absence of a certain target material such as blood, is an invaluable tool. Among these tests, there are different specificities, sensitivities, and shelf lives. The accuracy of a test is an algebraic combination of the specificity and sensitivity of the test. Each test has limitations as given by its false positive and false negative rates. The aim of this study was to illustrate how the false positive and false negative rates are to be properly determined using a simulation study for the phenolphthalein test. New presumptive tests must be properly evaluated/validated through testing of commonly encountered household items and other potentially probative items usually found at crime scenes, however, the makeup of test sets must appropriately capture all error rates. In order to correctly use these results when the test is applied to an unknown sample recovered at a crime scene, the error rates cannot be applied directly to estimate whether or not the sample is actually the analyte of interest. In a validation study, the forensic scientist calculates the false positive rate as the p(Positive Reaction|Blood), whereas at the scene, the crime scene investigator wishes to determine the p(Blood|Positive Reaction). All crime scene investigators need to ensure that the conditional is not transposed when interpreting such results. Furthermore, this work provides a model for the assessment of a multiple test diagnostic system intended for investigators.     

Prison gang integration and inmate violence

Purpose

Gang membership has been linked in a number of prior studies with inmate misconduct; known gang members are more prone than non-gang members to act violently behind bars. Theories of intergroup conflict suggest, however, there is reason to expect that broader within-prison gang dynamics, not just gang membership alone, are associated with the incidence of violence.

Methods

We collected data on inmates from a large southern state and estimated multilevel models of inmate-on-inmate violence. Included in our models were a variety of common individual-level correlates of violent misconduct, among them gang membership. Substantive prison-level correlates included the percentage of gang members and “gang integration,” the latter being a measure of gang heterogeneity.

Results

We found a modest association between both gang variables and inmate-on-inmate violence, with gang integration being the most significant of the two.

Conclusions

Gang membership is an important correlate of inmate violence, but attention to broader prison gang dynamics is clearly necessary. We discuss the implications of this finding for theories of inmate violence.