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Copy file name to clipboardExpand all lines: paper/paper.md
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- name: Malavika Mukundan
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- name: Jennifer Wang
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affiliations:
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- name: Metric Geometry and Gerrymandering Group (MGGG)
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- name: MGGG Redistricting Lab
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index: 1
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bibliography: paper.bib
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---
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Reform advocates also need to describe voting mechanisms and their
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likely outcomes effectively to members of their communities. The end-to-end pipeline provided by `VoteKit` allows advocates to toggle different system settings and compare expected outcomes. For example,
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Figure 1 comes from a report on reform proposals for the chambers of the Washington state legislature, with Systems 0-3 as paired bicameral systems and Systems 4-5 as unicameral solutions. Using the codebase that formed the foundation of `VoteKit`, researchers compared the expected outcomes for minority representation under these six systems.
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Figure 1 is reprinted from a report on reform proposals for the chambers of the Washington state legislature, with Systems 0-3 as paired bicameral systems and Systems 4-5 as unicameral solutions. Using the codebase that formed the foundation of `VoteKit`, researchers compared the expected outcomes for minority representation under these six systems.
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![A comparison of a variety of electoral systems and their affect on minority representation in a case study of the Washington state legislature [@washington_leg].](./figures/WA_poc_seats_chartsystem_compare_pared.png){width=100%}
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![A comparison of a variety of electoral systems and their effect on minority representation in a case study of reform proposals for the Washington state legislature [@washington_leg].](./figures/WA_poc_seats_chartsystem_compare_pared.png){width=100%}
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## Area of need: Resources for research
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# Projects
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A significant number of white papers and scholarly articles have used
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`VoteKit`(and its predecessor codebase) in recent years. These include
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`VoteKit`(and its predecessor codebase) in recent years. These include
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the following.
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- A large number of case studies in ranked-choice modeling, such as
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include Brenda Macias, Emarie De La Nuez, Greg Kehne, Jordan Phan, Rory
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Erlich, James Turk, and David McCune. Earlier code contributions were
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made by Chanel Richardson, Anthony Pizzimenti, Gabe Schoenbach, Dylan
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Phelan, Thomas Weighill, Dara Gold, and Amy Becker. The authors also
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thank Deb Otis, Peter Rock, Jeanne Clelland, and Michael Parsons for
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Phelan, Thomas Weighill, Dara Gold, and Amy Becker; recent code contributions
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also come from Peter Rock, Kevin Quinn, and Divij Sinha. The authors also
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thank Deb Otis, Jeanne Clelland, and Michael Parsons for
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helpful feedback. FairVote's data repository in Dataverse
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(<https://dataverse.harvard.edu/dataverse/rcv_cvrs>) and RCV Cruncher
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code on GitHub (<https://github.com/fairvotereform/rcv_cruncher/>) are
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