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      Legislators’ roll-call voting behavior increasingly corresponds to intervals in the political spectrum

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      1 , 2 ,
      Scientific Reports
      Nature Publishing Group UK
      Computational science, Complex networks

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          Abstract

          Scaling techniques such as the well known NOMINATE position political actors in a low dimensional space to represent the similarity or dissimilarity of their political orientation based on roll-call voting patterns. Starting from the same kind of data we propose an alternative, discrete, representation that replaces positions (points and distances) with niches (boxes and overlap). In the one-dimensional case, this corresponds to replacing the left-to-right ordering of points on the real line with an interval order. As it turns out, this seemingly simplistic one-dimensional model is sufficient to represent the similarity of roll-call votes by U.S. senators in recent years. In a historic context, however, low dimensionality represents the exception which stands in contrast to what is suggested by scaling techniques.

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          Most cited references28

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          The Statistical Analysis of Roll Call Data

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            Partisans without Constraint: Political Polarization and Trends in American Public Opinion

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              Incidence matrices and interval graphs

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                Author and article information

                Contributors
                lubrandes@ethz.ch
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                15 October 2020
                15 October 2020
                2020
                : 10
                : 17369
                Affiliations
                [1 ]GRID grid.5379.8, ISNI 0000000121662407, The Mitchell Centre for Social Network Analysis, , The University of Manchester, ; Manchester, M13 9PL UK
                [2 ]GRID grid.5801.c, ISNI 0000 0001 2156 2780, Social Networks Lab, Department of Humanities, Social and Political Sciences, , ETH Zürich, ; Zürich, Switzerland
                Article
                74175
                10.1038/s41598-020-74175-w
                7566643
                33060656
                c6686e37-9c02-4603-81ee-89b1e5940450
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 23 June 2020
                : 14 September 2020
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                computational science,complex networks
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                computational science, complex networks

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