3 resultados para Devonshire Association for the Advancement of Science, Literature and Art
em Duke University
Resumo:
*Designated as an exemplary master's project for 2015-16*
This paper examines how contemporary literature contributes to the discussion of punitory justice. It uses close analysis of three contemporary novels, Margaret Atwood’s The Heart Goes Last, Hillary Jordan’s When She Woke, and Joyce Carol Oates’s Carthage, to deconstruct different conceptions of punitory justice. This analysis is framed and supported by relevant social science research on the concept of punitivity within criminal justice. Each section examines punitory justice at three levels: macro, where media messages and the predominant social conversation reside; meso, which involves penal policy and judicial process; and micro, which encompasses personal attitudes towards criminal justice. The first two chapters evaluate works by Atwood and Jordan, examining how their dystopian schemas of justice shed light on top-down and bottom-up processes of punitory justice in the real world. The third chapter uses a more realistic novel, Oates’s Carthage, to examine the ontological nature of punitory justice. It explores a variety of factors that give rise to and legitimize punitory justice, both at the personal level and within a broader cultural consensus. This chapter also discusses how both victim and perpetrator can come to stand in as metaphors to both represent and distract from broader social issues. As a whole, analysis of these three novels illuminate how current and common conceptualizations of justice have little to do with the actual act of transgression itself. Instead, justice emerges as a set of specific, conditioned responses to perceived threats, mediated by complex social, cultural, and emotive forces.
Resumo:
The work presented in this dissertation is focused on applying engineering methods to develop and explore probabilistic survival models for the prediction of decompression sickness in US NAVY divers. Mathematical modeling, computational model development, and numerical optimization techniques were employed to formulate and evaluate the predictive quality of models fitted to empirical data. In Chapters 1 and 2 we present general background information relevant to the development of probabilistic models applied to predicting the incidence of decompression sickness. The remainder of the dissertation introduces techniques developed in an effort to improve the predictive quality of probabilistic decompression models and to reduce the difficulty of model parameter optimization.
The first project explored seventeen variations of the hazard function using a well-perfused parallel compartment model. Models were parametrically optimized using the maximum likelihood technique. Model performance was evaluated using both classical statistical methods and model selection techniques based on information theory. Optimized model parameters were overall similar to those of previously published Results indicated that a novel hazard function definition that included both ambient pressure scaling and individually fitted compartment exponent scaling terms.
We developed ten pharmacokinetic compartmental models that included explicit delay mechanics to determine if predictive quality could be improved through the inclusion of material transfer lags. A fitted discrete delay parameter augmented the inflow to the compartment systems from the environment. Based on the observation that symptoms are often reported after risk accumulation begins for many of our models, we hypothesized that the inclusion of delays might improve correlation between the model predictions and observed data. Model selection techniques identified two models as having the best overall performance, but comparison to the best performing model without delay and model selection using our best identified no delay pharmacokinetic model both indicated that the delay mechanism was not statistically justified and did not substantially improve model predictions.
Our final investigation explored parameter bounding techniques to identify parameter regions for which statistical model failure will not occur. When a model predicts a no probability of a diver experiencing decompression sickness for an exposure that is known to produce symptoms, statistical model failure occurs. Using a metric related to the instantaneous risk, we successfully identify regions where model failure will not occur and identify the boundaries of the region using a root bounding technique. Several models are used to demonstrate the techniques, which may be employed to reduce the difficulty of model optimization for future investigations.
