Schools Without Diversity: Education Management Organizations, Charter Schools, and the Demographic Stratification of the American School System

This report, which is a comprehensive examination of enrollment patterns in charter schools operated by Education Management organizations (EMOs), finds that charter schools run by EMOs are segregated by race, family income, disabilities and English language learner status as compared with their local public schools districts

Geometric properties of Lagrangian mechanical systems

The geometry of a Lagrangian mechanical system is determined by its associated evolution semispray. We uniquely determine this semispray using the symplectic structure and the energy of the Lagrange space and the external force field. We study the variation of the energy and Lagrangian functions along the evolution and the horizontal curves and give conditions by which these variations vanish. We provide examples of mechanical systems which are dissipative and for which the evolution nonlinear connection is either metric or symplectic

Robustness of Random Forest-based gene selection methods

Gene selection is an important part of microarray data analysis because it provides information that can lead to a better mechanistic understanding of an investigated phenomenon. At the same time, gene selection is very difficult because of the noisy nature of microarray data. As a consequence, gene selection is often performed with machine learning methods. The Random Forest method is particularly well suited for this purpose. In this work, four state-of-the-art Random Forest-based feature selection methods were compared in a gene selection context. The analysis focused on the stability of selection because, although it is necessary for determining the significance of results, it is often ignored in similar studies. The comparison of post-selection accuracy in the validation of Random Forest classifiers revealed that all investigated methods were equivalent in this context. However, the methods substantially differed with respect to the number of selected genes and the stability of selection. Of the analysed methods, the Boruta algorithm predicted the most genes as potentially important. The post-selection classifier error rate, which is a frequently used measure, was found to be a potentially deceptive measure of gene selection quality. When the number of consistently selected genes was considered, the Boruta algorithm was clearly the best. Although it was also the most computationally intensive method, the Boruta algorithm's computational demands could be reduced to levels comparable to those of other algorithms by replacing the Random Forest importance with a comparable measure from Random Ferns (a similar but simplified classifier). Despite their design assumptions, the minimal optimal selection methods, were found to select a high fraction of false positives

Political knowledge and voter turnout

This paper examines the relationship between a voter's level of political knowledge and the choice to vote. The issue of voter turnout is one of the major topics in American politics and has been studied extensively. This study seeks to fill a gap in the current body of academic research that fails to account for the significance of political knowledge as a predictor of voter turnout. Since political knowledge is, in part, a product of many other variables (including both socio-demographic and psychological), it serves as a single predictor that can encompass elements of other predictors. Using 2004 NES data, this study shows that an increase in campaign political knowledge is significantly correlated with an increase in the likelihood that the respondent will vote.Department of Political ScienceThesis (M.A.