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Record W7043338604

SHAPING A MORE EQUITABLE ELECTION SYSTEM: A CANADIAN APPROACH TO SOLVING THE VOTING RIGHTS CRISIS IN AMERICA

2025· article· en· W7043338604 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicLaw, Rights, and Freedoms
Canadian institutionsnot available
Fundersnot available
KeywordsVotingProportionality (law)First-past-the-post votingDisapproval votingStraight-ticket votingRanked voting systemTest (biology)
DOInot available

Abstract

fetched live from OpenAlex

In 1965, the Voting Rights Act was passed, ushering in a new era of voting freedom. The Act brought an end to many of the overtly discriminatory practices that had persisted for nearly two centuries. Over time, however, states began to introduce more subtle and complex voting regulations that gradually undermined the gains achieved through the civil rights movement. In 2021, the Supreme Court dismantled an essential safeguard for voters, significantly weakening the protections the Act once guaranteed. This erosion of protections is largely attributable to a single doctrinal standard within the Act, known as the totality of the circumstances test, which determines if voting policies are discriminatory. By analyzing voting regulations individually, rather than in their broader context, this test often fails to capture the cumulative and systemic effects of multiple laws that, together, can suppress voter participation. This Note explores the proportionality test used in Canada to evaluate potentially discriminatory voting laws. This test offers a more rigorous and structured analytical framework that is better equipped to assess how seemingly neutral laws may disproportionately impact minority groups. As a result, it provides a more consistent and equitable means of protecting voting rights. Ultimately, this Note advocates for amending the Voting Rights Act to replace the totality of the circumstances test with the proportionality test.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.970
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.268
Teacher spread0.249 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations0
Published2025
Admission routes1
Has abstractyes

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