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Electronic Voting Systems

2019· reference-entry· en· W4252010929 on OpenAlexaboutno aff
Greg Vonnahme

Bibliographic record

VenuePolitical Science · 2019
Typereference-entry
Languageen
FieldComputer Science
TopicInternet Traffic Analysis and Secure E-voting
Canadian institutionsnot available
Fundersnot available
KeywordsElectronic votingBallotVotingDisapproval votingCardinal voting systemsComputer securityBullet votingComputer scienceInternet privacyProxy votingStraight-ticket votingPolitical scienceLawPolitics

Abstract

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In 2001, Wand and colleagues published a paper titled “The Butterfly Did It” (see Wand, et al. 2001, cited under Voting System Neutrality) in which they argue that Palm Beach County’s butterfly ballot caused enough errors to decide the 2000 election for George W. Bush. The butterfly ballot also helped launch significant new research initiatives into voting systems and prompted new federal legislation through the Help America Vote Act of 2002, which served to modernize American voting systems. Along with Internet voting, these developments account for most contemporary research on electronic voting systems. Research on electronic voting systems is now at a crossroads. Much of the research following the 2000 election evaluated technology including lever and punch-card machines that are now largely obsolete (Stewart 2011, cited under History and Development of Voting Systems). Current and future research is moving in the direction of issues of security, Internet voting, ballot design, usability, efficiency, and cost of electronic voting systems. All voting systems in the United States today are electronic to a degree. Ansolabehere and Persily 2010 (cited under Empirical and Legal Evaluation of Voting Systems) identifies three discrete parts to voting systems: voter authentication, vote preparation, and vote management. Electronic voting technology can facilitate any of these steps. The term “electronic voting” is polysemous. Electronic voting (or e-voting) variously describes direct-recording electronic voting, electronic vote tabulation, or Internet voting among others. This document defines electronic voting as any voting system that uses electronic technology at any step in the voting process. Fully electronic voting systems use DREs (direct-recording electronic machines), in which ballots are electronically generated, prepared, and counted. Hybrid types of electronic voting are optically scanned ballots (precinct or centrally counted) or ballot mark devices (BMDs), which the voter completes manually and submits but is electronically counted. Electronic voting systems can also include Internet voting in which voters receive, prepare, and submit ballots online. The 2000 presidential election precipitated the most sweeping changes to voting systems, and we continue to see officials adopt new voting systems and Internet voting pilot programs, such as those in Estonia, Canada, Brazil, and Switzerland. Voting systems, particularly Internet voting, are a source of controversy in the United States and abroad. Debates over security and ease of use involve complex technologies and core democratic principles about the rights and responsibilities of citizens. Elections are also, at least in a narrow sense and especially in the United States, zero-sum. Only one person can hold an office, and any change in voting systems that helps one candidate or party necessarily harms the electoral prospects of others. At best, this leads officials to closely scrutinize new voting systems. At worst, it can lead to irreconcilable and unprincipled polarization over questions of voting technology. E-voting involves issues of technology, democratic participation, and electoral politics. This creates a rich environment for research on voting systems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.141
Threshold uncertainty score0.472

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0030.002
Scholarly communication0.0080.010
Open science0.0020.006
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.1410.128

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.269
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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