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Record W4206353349 · doi:10.1017/s0003055421001337

Enfranchisement and Incarceration after the 1965 Voting Rights Act

2022· article· en· W4206353349 on OpenAlexfundno aff
Nicholas Eubank, Adriane Fresh

Bibliographic record

VenueAmerican Political Science Review · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsnot available
FundersUniversity of CambridgeYork UniversityJohns Hopkins UniversityHarvard UniversityAmerican Political Science Association
KeywordsPrisonVotingMass incarcerationState (computer science)PoliticsVoter registrationRace (biology)Political sciencePower (physics)CriminologyWhite (mutation)SociologyLawGender studies

Abstract

fetched live from OpenAlex

The 1965 Voting Rights Act (VRA) fundamentally changed the distribution of electoral power in the US South. We examine the consequences of this mass enfranchisement of Black people for the use of the carceral state—police, the courts, and the prison system. We study the extent to which white communities in the US South responded to the end of Jim Crow by increasing the incarceration of Black people. We test this with new historical data on state and county prison intake data by race (~1940–1985) in a series of difference-in-differences designs. We find that states covered by Section 5 of the VRA experienced a differential increase in Black prison admissions relative to those that were not covered and that incarceration varied systematically in proportion to the electoral threat posed by Black voters. Our findings indicate the potentially perverse consequences of enfranchisement when establishment power seeks—and finds—other outlets of social and political control.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.369
Teacher spread0.346 · 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 designObservational
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

Citations38
Published2022
Admission routes1
Has abstractyes

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