BARD: Better Automated Redistricting

BARD is the first (and at time of writing, only) open source software package for general redistricting and redistricting analysis. BARD provides methods to create, display, compare, edit, automatically refine, evaluate, and profile political districting plans. BARD aims to provide a framework for scientific analysis of redistricting plans and to facilitate wider public participation in the creation of new plans. BARD facilitates map creation and refinement through command-line, graphical user interface, and automatic methods. Since redistricting is a computationally complex partitioning problem not amenable to an exact optimization solution, BARD implements a variety of selectable metaheuristics that can be used to refine existing or randomly-generated redistricting plans based on user-determined criteria. Furthermore, BARD supports automated generation of redistricting plans and profiling of plans by assigning different weights to various criteria, such as district compactness or equality of population. This functionality permits exploration of trade-offs among criteria. The intent of a redistricting authority may be explored by examining these trade-offs and inferring which reasonably observable plans were not adopted. Redistricting is a computationally-intensive problem for even modest-sized states. Performance is thus an important consideration in BARD's design and implementation. The program implements performance enhancements such as evaluation caching, explicit memory management, and distributed computing across snow clusters.

accuracy: Tools for Accurate and Reliable Statistical Computing

Most empirical social scientists are surprised that low-level numerical issues in software can have deleterious effects on the estimation process. Statistical analyses that appear to be perfectly successful can be invalidated by concealed numerical problems. We have developed a set of tools, contained in accuracy, a package for R and S-PLUS, to diagnose problems stemming from numerical and measurement error and to improve the accuracy of inferences. The tools included in accuracy include a framework for gauging the computational stability of model results, tools for comparing model results, optimization diagnostics, and tools for collecting entropy for true random numbers generation.

BARD: Better Automated Redistricting

BARD is the first (and at time of writing, only) open source software package for general redistricting and redistricting analysis. BARD provides methods to create, display, compare, edit, automatically refine, evaluate, and profile political districting plans. BARD aims to provide a framework for scientific analysis of redistricting plans and to facilitate wider public participation in the creation of new plans. BARD facilitates map creation and refinement through command-line, graphical user interface, and automatic methods. Since redistricting is a computationally complex partitioning problem not amenable to an exact optimization solution, BARD implements a variety of selectable metaheuristics that can be used to refine existing or randomly-generated redistricting plans based on user-determined criteria. Furthermore, BARD supports automated generation of redistricting plans and profiling of plans by assigning different weights to various criteria, such as district compactness or equality of population. This functionality permits exploration of trade-offs among criteria. The intent of a redistricting authority may be explored by examining these trade-offs and inferring which reasonably observable plans were not adopted. Redistricting is a computationally-intensive problem for even modest-sized states. Performance is thus an important consideration in BARD's design and implementation. The program implements performance enhancements such as evaluation caching, explicit memory management, and distributed computing across snow clusters

accuracy: Tools for Accurate and Reliable Statistical Computing

Most empirical social scientists are surprised that low-level numerical issues in software can have deleterious effects on the estimation process. Statistical analyses that appear to be perfectly successful can be invalidated by concealed numerical problems. We have developed a set of tools, contained in accuracy, a package for R and S-PLUS, to diagnose problems stemming from numerical and measurement error and to improve the accuracy of inferences. The tools included in accuracy include a framework for gauging the computational stability of model results, tools for comparing model results, optimization diagnostics, and tools for collecting entropy for true random numbers generation

CSF Testing for Multiple Sclerosis

New diagnostic criteria for multiple sclerosis (MS) were published in 2001 by McDonald and colleagues. These criteria take account of the clinical features, brain and spinal-cord MRI findings, CSF findings, and visual evoked-potential studies. The McDonald criteria define rigorous MRI requirements but do not define an optimum CSF test for the diagnosis of MS. CSF testing should be optimised, because, in the McDonald criteria, a positive CSF study is an essential diagnostic criterion in patients who have objective clinical evidence of only one lesion and only a few MRI lesions, and it is a mandatory criterion for the diagnosis of primary progressive MS

Learning Theory Expertise in the Design of Learning Spaces: Who Needs a Seat at the Table?

This study highlights the impact of including stakeholders with expertise in learning theory in a learning space design process. We present the decision-making process during the design of the Krause Innovation Studio on the campus of the Pennsylvania State University to draw a distinction between the architect and faculty member’s decision-making process. Often, the architect relied on guiding principles such as flexibility, while the faculty member drew from learning theory such as the sociocultural theory of learning (Vygotsky, 1978). This study demonstrates the value of learning theory expertise and includes suggestions and possible implications for future designs of learning spaces

The Fruitcake Capital of the World

Two different fruitcake companies in the same small town are discussed and their widely different strategies are presented. Each of the entrepreneurs began working with a “master baker” as little boys but later followed different paths to develop a successful fruitcake business. A brief history of the fruitcake is offered as evidence that the product has been around for hundreds of years and will likely not go away anytime soon in spite of the ridicule and humor that has surrounded fruitcakes during the past twenty-five years