Graduate Training, Current Affiliation and Publishing Books in Political Science

Scores of studies have measured the quality of political science departments. Generally speaking, these studies have taken two forms. Many have relied on scholars\u27 survey responses to construct rankings of the major departments. For example, almost 50 years ago Keniston (1957) interviewed 25 department chairpersons and asked them to assess the quality of various programs, and, much more recently, the National Research Council (NRC 1995) asked 100 political scientists to rate the “scholarly quality of program faculty” in the nation\u27s political science doctoral departments. In response to these opinion-based rankings, a number of researchers have developed what they claim to be more objective measures of department quality based on the research productivity of the faculty (Ballard and Mitchell 1998; Miller, Tien, and Peebler 1996; Robey 1979). While department rankings using these two methods are often similar, there are always noteworthy differences and these have generated an additional literature that explores the relationship between the rating systems (Garand and Graddy 1999; Jackman and Siverson 1996; Katz and Eagles 1996; Miller, Tien, and Peebler 1996)

Receipt from Benj. Gillespie to P. McCormick and Receipt from Peter McCormick to Ogden Goelet

https://digitalcommons.salve.edu/goelet-new-york/1013/thumbnail.jp

Urban Ecology and the Effectiveness of Aedes Control

Past initiatives to control Aedes mosquitoes were successful, in part because they implemented draconian top-down control programs. To achieve similar results now, explicit recognition of the complexity in urban ecologies in terms of land ownership, law enforcement and accessibility for control interventions are required. By combining these attributes, four classes of spaces, along with corresponding control strategies, are suggested to better target Aedes species population control efforts. On one end of the spectrum there are accessible and accountable spaces (e.g. backyards and closely managed public facilities), where interventions can rely predominantly on bottom-up strategies with the local population playing the principle role in the implementation of actions, but with government coordination. On the other end of the spectrum are inaccessible and unaccountable spaces, which require top-down and extensive approaches. By identifying these and the intermediate classes of space, government and private resources can be allocated in a more efficient customized manner. Based on this new framework, a set of actions is proposed that might be implemented in dengue and other Aedes-borne crises. The framework considers existing limitations and opportunities associated with modern societies–which are fundamentally different from those associated with the successful control of Aedes species in the past

Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model

We aim to produce predictive models that are not only accurate, but are also interpretable to human experts. Our models are decision lists, which consist of a series of if … then. . . statements (e.g., if high blood pressure, then stroke) that discretize a high-dimensional, multivariate feature space into a series of simple, readily interpretable decision statements. We introduce a generative model called Bayesian Rule Lists that yields a posterior distribution over possible decision lists. It employs a novel prior structure to encourage sparsity. Our experiments show that Bayesian Rule Lists has predictive accuracy on par with the current top algorithms for prediction in machine learning. Our method is motivated by recent developments in personalized medicine, and can be used to produce highly accurate and interpretable medical scoring systems. We demonstrate this by producing an alternative to the CHADS₂ score, actively used in clinical practice for estimating the risk of stroke in patients that have atrial fibrillation. Our model is as interpretable as CHADS₂, but more accurate.National Science Foundation (U.S.) (Grant IIS-1053407

An Interpretable Stroke Prediction Model using Rules and Bayesian Analysis

We aim to produce predictive models that are not only accurate, but are also interpretable to human experts. Our models are decision lists, which consist of a series of if...then... statements (for example, if high blood pressure, then stroke) that discretize a high-dimensional, multivariate feature space into a series of simple, readily inter- pretable decision statements. We introduce a generative model called the Bayesian List Machine which yields a posterior distribution over possible decision lists. It employs a novel prior structure to encourage sparsity. Our experiments show that the Bayesian List Machine has predictive accuracy on par with the current top algorithms for prediction in machine learning. Our method is motivated by recent developments in personalized medicine, and can be used to produce highly accurate and interpretable medical scoring systems. We demonstrate this by producing an alternative to the CHADS2 score, actively used in clinical practice for estimating the risk of stroke in patients that have atrial brillation. Our model is as interpretable as CHADS2, but more accurate