Fitness to stand trial : how discourses of rationality, reasonableness and culpability inform the law and psychiatry to create standards of normal

The objective of this thesis is to demonstrate the importance of the concepts of rationality, reasonableness, culpability and autonomy that inform and support our conception of both the person and the punishable subject. A critical discourse analysis tracing these concepts through both the law and psychological tools used to evaluate the fitness of a person reveals that these concepts and their implied values are inconsistently applied to the mentally disordered who come into conflict with the law. I argue that the result of this inconsistency compromises a person's autonomy which is a contradiction to this concept as a foundational principle of the law. Ultimately, this thesis does not provide a solution to be employed in policy making, but its analysis leaves open possibilities for further exploration into the ways legal and social justice can be reconciled.

Using a Bayesian model for bankruptcy prediction : a comparative approach

The purpose of this study is to examine the impact of the choice of cut-off points, sampling procedures, and the business cycle on the accuracy of bankruptcy prediction models. Misclassification can result in erroneous predictions leading to prohibitive costs to firms, investors and the economy. To test the impact of the choice of cut-off points and sampling procedures, three bankruptcy prediction models are assessed- Bayesian, Hazard and Mixed Logit. A salient feature of the study is that the analysis includes both parametric and nonparametric bankruptcy prediction models. A sample of firms from Lynn M. LoPucki Bankruptcy Research Database in the U. S. was used to evaluate the relative performance of the three models. The choice of a cut-off point and sampling procedures were found to affect the rankings of the various models. In general, the results indicate that the empirical cut-off point estimated from the training sample resulted in the lowest misclassification costs for all three models. Although the Hazard and Mixed Logit models resulted in lower costs of misclassification in the randomly selected samples, the Mixed Logit model did not perform as well across varying business-cycles. In general, the Hazard model has the highest predictive power. However, the higher predictive power of the Bayesian model, when the ratio of the cost of Type I errors to the cost of Type II errors is high, is relatively consistent across all sampling methods. Such an advantage of the Bayesian model may make it more attractive in the current economic environment. This study extends recent research comparing the performance of bankruptcy prediction models by identifying under what conditions a model performs better. It also allays a range of user groups, including auditors, shareholders, employees, suppliers, rating agencies, and creditors' concerns with respect to assessing failure risk.