Finding Life in Hurricane Shelby: Reviving the Voting Rights Act by Reforming Section 3 Preclearance
Bibliographic record
Abstract
INTRODUCTIONAlberta Currie, a seventy-eight-year-old African-American woman, has voted in every election since 1956 when she was just twenty-one years old. 1 Long ago, Alberta's grandmother instilled in her the importance of never missing a voting day. 2 Unfortunately, the 2012 general election may have been Alberta's last opportunity.Alberta does not possess a birth certificate because she was born at home to a midwife in the segregated South during the height of Jim Crow. 3 For this reason, Alberta cannot obtain photo identification recognized under North Carolina law. 4 In most states, Alberta's lack of photo identification would not prevent her from exercising her right to vote.But in North Carolina, Alberta may never vote again, due to the state's strict voter identification law enacted in the aftermath of the Supreme Court's decision in Shelby County v. Holder.5 Although Shelby County's critics attacked the decision as the end of the Voting Rights Era, 6 Alberta's circumstances demonstrate that a new war over the Voting Rights Act (the Act) has only just begun.7 At the heart of this * J.D. Candidate, The Ohio State University Moritz College of Law, expected
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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".