Identifying the Median Justice on the Supreme Court through Multidimensional Scaling: Analysis of ``Natural Courts'' 1953–1991

Grofman, Bernard; Brazill, Timothy J.

doi:10.1023/a:1015601614637

Cited by 41 publications

(35 citation statements)

References 54 publications

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“…However, it has become increasingly common for researchers to assume that a single ideological dimension underlies the Justices' voting in all cases (e.g., Ferejohn & Weingast 1992;Groseclose & Schiavoni 2001;Hettinger & Zorn 2005). The frequently cited and employed Martin-Quinn ideology scores are based on this assumption (Martin & Quinn 2002), and Supreme Court voting data support the single dimension solution for many cases (Grofman & Brazill 2002;Poole 2003).…”

Section: Ideological Voting Disorder At the Supreme Courtmentioning

confidence: 99%

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Measuring Deviations from Expected Voting Patterns on Collegial Courts

Edelman

Klein

Lindquist

2008

J Empirical Legal Studies

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Even where idiosyncratic factors such as ideology play large and consistent roles in judges' decision making, there are always cases where the patterns of judges' votes confound our expectations. In some ways, these are among the most interesting cases for scholars, raising important questions about judicial behavior and institutions. In the first part of this article, we introduce a quantitative measure of deviations from expected voting patterns intended to allow scholars to systematically study unexpected votes and the questions they raise. In the second part, we illustrate the use of this new measure by developing a preliminary model to predict departures from strict ideological voting in the U.S. Supreme Court in an effort to distinguish alternative explanations for these patterns. Although the conventional wisdom might suggest that disordered voting occurs because of cross‐cutting issues, we find little evidence that case complexity is responsible for unexpected coalitions. This result suggests that some alternative explanation exists for such voting behavior; we offer several possibilities—including the influence of legal considerations—in our conclusions.

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Section: Ideological Voting Disorder At the Supreme Courtmentioning

confidence: 99%

“…Grofman & Brazill 2002;Epstein & Segal 2006). First, it is important to control for case factors that might lead Justices to disagree about the locations of policy alternatives.…”

Section: Deviations From Expected Voting Patterns On Collegial Courtsmentioning

confidence: 99%

Measuring Deviations from Expected Voting Patterns on Collegial Courts

Edelman

Klein

Lindquist

2008

J Empirical Legal Studies

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“…The second dataset is a matrix of votes from the United States Supreme Court, including 13 judges and their votes on 1,596 cases. Some political scientists (35) have argued that a unidimensional structure best accounts for variation in Supreme Court data and in political beliefs more generally, although other structural forms [including higher-dimensional spaces (36) and sets of clusters (37)] have also been proposed. Consistent with the unidimensional hypothesis, our model identifies the chain as the best-scoring form for the Supreme Court data.…”

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confidence: 99%

The discovery of structural form

Kemp

Tenenbaum

2008

Proc. Natl. Acad. Sci. U.S.A.

440

441

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Algorithms for finding structure in data have become increasingly important both as tools for scientific data analysis and as models of human learning, yet they suffer from a critical limitation. Scientists discover qualitatively new forms of structure in observed data: For instance, Linnaeus recognized the hierarchical organization of biological species, and Mendeleev recognized the periodic structure of the chemical elements. Analogous insights play a pivotal role in cognitive development: Children discover that object category labels can be organized into hierarchies, friendship networks are organized into cliques, and comparative relations (e.g., ''bigger than'' or ''better than'') respect a transitive order. Standard algorithms, however, can only learn structures of a single form that must be specified in advance: For instance, algorithms for hierarchical clustering create tree structures, whereas algorithms for dimensionality-reduction create low-dimensional spaces. Here, we present a computational model that learns structures of many different forms and that discovers which form is best for a given dataset. The model makes probabilistic inferences over a space of graph grammars representing trees, linear orders, multidimensional spaces, rings, dominance hierarchies, cliques, and other forms and successfully discovers the underlying structure of a variety of physical, biological, and social domains. Our approach brings structure learning methods closer to human abilities and may lead to a deeper computational understanding of cognitive development.cognitive development ͉ structure discovery ͉ unsupervised learning

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“…It is diffcult to think of a major debate within judicial politics that is not potentially touched by this problem. Scholars rely on observed Supreme Court cases to study the preferences of Supreme Court Justices (Grofman & Brazill 2002;Martin & Quinn 2002;Segal & Spaeth 2002), external constraints on their decision making (Spiller & Gely 1992;Segal 1997;Bergara et al 2003), the treatment of case facts (Segal 1984;McGuire 1990;Hagle 1991;Ignagni 1994), the role of law (George & Epstein 1992;, and ideological change on the Court (Epstein et al 1998(Epstein et al , 2007bMartin & Quinn 2007). At stake, therefore, are many of the most important and controversial substantive debates in judicial politics.…”

Section: Introductionmentioning

confidence: 99%