SHAPING A MORE EQUITABLE ELECTION SYSTEM: A CANADIAN APPROACH TO SOLVING THE VOTING RIGHTS CRISIS IN AMERICA
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.035 | 0.016 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".