The maintenance of the size and strength of the U.S. volunteer military heavily depends on its recruitment arm to prospect and inform potential recruits. In a country where military service is believed to offer benefits such as steady incomes and college education to disadvantaged communities while implying they are unavailable through other means the concern becomes that recruiters target these communities for recruitment. This thesis first draws on the existing literature about citizen-state interactions to derive a new theoretical perspective about predatory practices then begins to evaluate that theory through a qualitative analysis of military recruitment manuals. The findings suggest that predatory practices may be occurring at lower levels of the recruitment hierarchy and those in charge are aware and unable or unwilling to prevent it.

With new 2,4-Dichlorophenoxyacetic acid (2,4-D) tolerant crops, increases in off-target movement events are expected. New formulations may mitigate these events, but standard lab techniques are ineffective in identifying these 2,4-D formulations. Using Fourier-transform infrared spectroscopy and machine learning algorithms, research was conducted to classify 2,4-D formulations in treated herbicide-tolerant soybeans and cotton and observe the influence of leaf treatment status and collection timing on classification accuracy. Pooled Classification models using k-nearest neighbor classified 2,4-D formulations with over 65% accuracy in cotton and soybean. Tissue collected 14 DAT and 21 DAT for cotton and soybean respectively produced higher accuracies than the pooled model. Tissue directly treated with 2,4-D also performed better than the pooled model. Lastly, models using timing and treatment status as factors resulted in higher accuracies, with cotton 14 DAT New Growth and Treated models and 28 DAT and 21 DAT Treated soybean models achieving the best accuracies.