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Record W4399177320 · doi:10.1177/26326663241255191

Incarceration and racism in the Americas: Notes for future internationally comparative research on racial inequality and imprisonment

2024· article· en· W4399177320 on OpenAlexaff
Caroline Mary Parker, Amaya Perez‐Brumer

Bibliographic record

VenueIncarceration · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsRacismImprisonmentMainlandLatin AmericansInequalityMainland ChinaCriminologySociologyGeographyPolitical scienceWhite (mutation)Gender studiesLaw

Abstract

fetched live from OpenAlex

This study presents the first ever comparative regional portrait of racial inequality and incarceration across the Americas, using census data from Brazil, Cuba, Puerto Rico, and the United States mainland. While racism is known to pervade criminal justice across the US mainland, Latin American prisons remain understudied, with the entire region often construed as racially harmonious and uniformly “mixed” rather than racially plural or stratified. Our findings reveal consistent underrepresentation of white individuals and overrepresentation of Black individuals in all countries. Mixed-race individuals in Brazil, Cuba, and Puerto Rico experience higher incarceration rates than whites but lower rates than Blacks. These findings challenge the conception that the US mainland is unique in its historically entrenched profile of structural racism, while highlighting varying degrees of racial inequality internationally. Whereas Cuba and the US mainland display relatively higher levels of racial inequality in imprisonment, Puerto Rico and Brazil display relatively lower levels.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.426
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.170
GPT teacher head0.502
Teacher spread0.332 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations4
Published2024
Admission routes1
Has abstractyes

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