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Titlebook: Smarter Ballots; Electoral Realism an J.S. Maloy Book 2019 The Editor(s) (if applicable) and The Author(s), under exclusive license to Spri

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发表于 2025-3-21 16:23:37 | 显示全部楼层 |阅读模式
书目名称Smarter Ballots
副标题Electoral Realism an
编辑J.S. Maloy
视频video
概述Offers a comparative overview of elections research on the US, UK, Australia, Spain, Ireland, etc.Translates the results of academic research on elections worldwide for non-specialist scholars.Provide
丛书名称Elections, Voting, Technology
图书封面Titlebook: Smarter Ballots; Electoral Realism an J.S. Maloy Book 2019 The Editor(s) (if applicable) and The Author(s), under exclusive license to Spri
描述.This book presents a new democratic theory of election reform, using the tradition of political realism to interrogate and synthesize findings from global elections research and voting theory. In a world of democratic deficits and uncivil societies, political researchers and reformers should prioritize creating smarter ballots before smarter voters. Many democracies’ electoral systems impose a dilemma of disempowerment which traps voters between the twin dangers of vote-splitting and “lesser evil” choices, restricting individual expression while degrading systemic accountability. The application of innovative conceptual tools to comparative empirical analysis and previous experimental results reveals that ballot structure is crucial, but often overlooked, in sustaining this dilemma. Multi-mark ballot structures can resolve the dilemma of disempowerment by allowing voters to rank or grade multiple parties or candidates per contest, thereby furnishing democratic citizens with a broader array of options, finer tools of expression, and stronger powers of accountability. Innovative proposals for ranking and grading ballots in both multi-winner and single-winner contests, including refe
出版日期Book 2019
关键词Electoral Reform; Multi-Mark Ballots; Single-Mark Ballots; Exclusive and Distributive Insput; Ballot Exp
版次1
doihttps://doi.org/10.1007/978-3-030-13031-2
isbn_softcover978-3-030-13033-6
isbn_ebook978-3-030-13031-2Series ISSN 2945-7610 Series E-ISSN 2945-7629
issn_series 2945-7610
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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发表于 2025-3-21 21:54:05 | 显示全部楼层
Smarter Ballots978-3-030-13031-2Series ISSN 2945-7610 Series E-ISSN 2945-7629
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Elections, Voting, Technologyhttp://image.papertrans.cn/s/image/869113.jpg
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https://doi.org/10.1007/978-3-030-13031-2Electoral Reform; Multi-Mark Ballots; Single-Mark Ballots; Exclusive and Distributive Insput; Ballot Exp
发表于 2025-3-22 11:38:03 | 显示全部楼层
J.S. MaloyOffers a comparative overview of elections research on the US, UK, Australia, Spain, Ireland, etc.Translates the results of academic research on elections worldwide for non-specialist scholars.Provide
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发表于 2025-3-22 19:47:23 | 显示全部楼层
J. S. Maloyesponding ML techniques that can be used for each task. It also covers challenges for the use of ML for security tasks and outlines research directions. . While many recent papers have proposed approaches for s978-3-031-28261-4978-3-031-28259-1Series ISSN 1945-9742 Series E-ISSN 1945-9750
发表于 2025-3-22 22:25:57 | 显示全部楼层
2945-7610 a broader array of options, finer tools of expression, and stronger powers of accountability. Innovative proposals for ranking and grading ballots in both multi-winner and single-winner contests, including refe978-3-030-13033-6978-3-030-13031-2Series ISSN 2945-7610 Series E-ISSN 2945-7629
发表于 2025-3-23 01:29:12 | 显示全部楼层
Book 2019e multiple parties or candidates per contest, thereby furnishing democratic citizens with a broader array of options, finer tools of expression, and stronger powers of accountability. Innovative proposals for ranking and grading ballots in both multi-winner and single-winner contests, including refe
发表于 2025-3-23 06:25:11 | 显示全部楼层
earning model, although the gradient may be used in some cases. These black-box or grey-box attacks suggest that many of the attack algorithms for deep learning may also be applicable to traditional machine learning models, which explains why we frequently refer to algorithms in the chapter when dis
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