These papers should be considered drafts. Comments are welcome.
You may also be interested in my dissertation or published papers
In this paper we reflect on on the crucial contributions innovations in statistical software have made to political methodology. And wee identify principles for writing statistical software with maximium benefit to the scholarly community.
The Data Preservation Alliance for the Social Sciences (Data-PASS) is a partnership of six major U.S. institutions with a strong focus on archiving social science research. The partnership is supported by an award from the Library of Congress through its National Digital Information Infrastructure and Preservation Program (NDIIPP). The goal of Data-PASS is to acquire and preserve data at-risk of being lost to the research community, from opinion polls, voting records, large-scale surveys, and other social science studies. This paper will discuss three of the significant products that have emerged from this partnership: (1) procedures for identifying and selecting "at risk" digital materials identified by the Partnership (2) the identification of at-risk social science data collections from individual researchers, as well as private research organizations, (3) the design and implementation of a shared catalog describing the data holdings of all partners. We conclude with some brief comments on the partners' future plans to develop an inter-archival syndicated storage service.
BARD provides a set of open source tools to automatically create and analyze redistricting plans. These tools support both scientific analysis of existing redistricting plans, and citizen participation in creating new plans.
Legislative intent and effect are intimately intertwined in redistricting lawsuits and academic research. Yet intent is impossible to measure directly and legislative statements of intent are often absent, ambiguous, or misleading. Academics and expert witnesses have offered a number of empirical approaches, such as searching for 'divergent boundary segments', attempting to create 'random' comparison plans and using estimated 'bias' and responsiveness to infer intent. In this article, we show that most of the methods currently in use are statistically biased, and the remainder fail to capture predominant intent.
Current voting jurisprudence and electoral institutions take into consideration only preferences over candidates, but neglect deeper preferences over assemblies. In "Social Choice in a Representative Democracy", Benoit and Kornhauser (1994) argue that "preferences over assemblies not candidates are fundamental" thus all election methods that are based on preferences for candidates are suspect, and they give powerful illustrations of the pitfalls of candidate-based voting procedures.
In this research note, I use complexity analysis and computer simulations to compare a number of assembly-based voting systems to their candidate-based counterparts. I find that assembly-based voting systems have serious limitations: Assembly-based voting systems that require full-preference orderings are computationally costly, hence impractical for elections in which candidates significantly outnumber seats. On the other hand, assembly-based systems that require only partial preference orderings can produce worse results than their candidate-based counterparts because assembly-based systems can multiply the number of possible choices in an election and exacerbate the voters' collective coordination problem
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