Finding Life in Hurricane Shelby: Reviving the Voting Rights Act by Reforming Section 3 Preclearance
Bibliographic record
Abstract
Following the Supreme Court’s decision in Shelby County v. Holder, states are now free to enact changes to voting laws that may burden voter’s access to the polls without fear of federal oversight. In fact, North Carolina and Texas have swiftly enacted controversial voter identification laws with stringent requirements. In response, the Justice Department has filed civil suits against these states under Section 2 of the Voting Rights Act, which authorizes private actions against discriminatory voting laws. Moreover, the Department of Justice asked the courts to “bail-in” these jurisdictions to preclearance under Section 3 of the Voting Rights Act, which allows judges to submit jurisdictions to preclearance if intentional discrimination is shown. However, the problem with Section 3’s preclearance mechanism is that its intentional discrimination requirement is overly burdensome for plaintiffs. In over forty years, only two states have been bailed-in to Section 3. On the other hand, federal courts have never utilized Section 3 to submit jurisdictions to preclearance in the context of voter identification laws. While a great deal of scholarly focus has been devoted to analyzing the role of Sections 4 and 5 of the Act, Section 3 has received very little attention from election law scholars, members of Congress, and federal judges. Accordingly, it is unclear how federal courts will respond to the Obama Administration’s request to bail-in these states to preclearance under Section 3. Thus, reform is greatly needed. This Note challenges conventional wisdom by arguing that Congress should abandon Section 5 preclearance by amending Section 3. Part II provides a background on the Voting Rights Act of 1965 and the Act’s most important mechanisms. Part III reviews how federal courts have interpreted the Act in numerous challenges brought against state and local voting laws. On the other hand, Part IV more closely examines the Supreme Court’s analysis in Shelby County. Specifically, Part IV argues that, based on the high standards of proving violations under Section 3, recent suits brought by the Department of Justice are inadequate in combating discriminatory voter identification laws. Part V concludes by proposing several amendments to Section 3 that lower the standard of proof required to submit jurisdictions to preclearance, clarify this Section’s scope, and change its initial evidentiary burden.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".