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Record W1604518897

Beyond the Margin of Litigation: Reforming U.S. Election Administration to Avoid Electoral Meltdown

2005· article· en· W1604518897 on OpenAlexaboutno aff
Richard L. Hasen

Bibliographic record

VenueSSRN Electronic Journal · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicLaw, Rights, and Freedoms
Canadian institutionsnot available
Fundersnot available
KeywordsPresidential electionPolitical scienceDemocracyAdministration (probate law)PoliticsLawElection lawPrimary electionSplit-ticket votingGeneral electionPublic administration
DOInot available

Abstract

fetched live from OpenAlex

In the 2004 presidential election, the United States came much closer to electoral meltdown, violence in the streets, and constitutional crisis than most people realize. Less than a 2% swing among Ohio voters toward Democratic candidate for President John Kerry and away from incumbent Republican President George W. Bush would have placed the Ohio - and national - election for president well within the margin of litigation, and it would have gotten ugly very quickly. Allegations of voter fraud and voter suppression were rampant on both sides, and even though Kerry conceded the election on the day after Election Day, public confidence in the U.S. system of American administration is now quite low. Previously unpublished data demonstrate that there is a growing partisan divide over views of the fairness of the election process.The bad news from the story of Election 2004 is that things likely won't get better in 2008. As Part I details, the extreme partisanship and close division of the American electorate, coupled with the Electoral College system, make the possibility of another razor-close presidential election in one or more battleground states fairly likely. Add to that mix election administration incompetence and a widely decentralized system of election administration with a patchwork of inconsistent rules. What's worse, since Bush v. Gore, losing candidates have become more willing to resort to election law as part of a political strategy: the number of election-law related cases in the lower courts has risen dramatically compared to the period before the case. It all adds up to a recipe for electoral meltdown.In Part II of this Article, I argue for three reforms that could significantly lower the risk of electoral meltdown. First, I advocate registration reform, in particular universal voter registration conducted by the government coupled with a voter identification program. There has been a wide partisan divide in the election administration debate between Democrats who have expressed concern about voter suppression and Republicans who have expressed concern about voter fraud. The registration reform I advocate can alleviate both of those concerns, minimize the potential for and political rhetoric regarding voter fraud, and eliminate a great majority of potential litigation surrounding presidential election administrationSecond, I advocate a transition to nonpartisan election administration. The nonpartisan solution aims to create both the actuality and appearance of neutrality in election administration, thereby bolstering the public's faith in the process. Australia and Canada serve as good models for reform in this regard, though not necessarily their nationalization of election administration. I consider how to assure that U.S. election administrators are truly nonpartisan, and contrast arguments for nonpartisan election administration with calls for nonpartisan redistricting commissions and campaign finance enforcement.Third, I discuss the role of the courts in minimizing electoral meltdown. The key here is to encourage courts to be more willing to entertain pre-election litigation and much more chary of entertaining post-election litigation. To the extent election administration problems can be recognized in advance, pre-election judicial review prevents future harm from occurring, rather than putting courts in the position of trying to undo the bad effects of a past harm. The costs of post-election review are large: the pressure put on courts to decide arcane election law questions when the outcome of an election - especially a presidential election - is huge, and the appearance of partisan decisionmaking is inevitable.

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)0.029
metaresearch head score (Gemma)0.060
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: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.060
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0180.010
Scholarly communication0.0190.016
Open science0.0030.012
Research integrity0.0110.017
Insufficient payload (model declined to judge)0.0090.003

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 head0.009
GPT teacher head0.274
Teacher spread0.265 · 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

Citations22
Published2005
Admission routes1
Has abstractyes

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