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Record W4405721291 · doi:10.1086/732194

To Protect and to Serve: Police, Power, and the Production of Inequality in the United States

2024· article· en· W4405721291 on OpenAlexfundno aff
Max Felker-Kantor

Bibliographic record

VenueCrime and Justice · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Deviance, and Social Control
Canadian institutionsnot available
FundersUniversity of CambridgePrinceton UniversityMcGill UniversityLouisiana State UniversityUniversity of OxfordHarvard UniversityYork UniversityUniversity of Pennsylvania
KeywordsInequalityProduction (economics)Power (physics)Political scienceComputer securityCriminologyLaw and economicsBusinessPsychologyComputer scienceSociologyEconomicsPhysicsMathematicsMicroeconomics

Abstract

fetched live from OpenAlex

The history of policing in the United States has garnered renewed attention over the past two decades. Historians have rethought the operation and function of police in American society. This body of scholarship, which can be characterized as a critical history of policing, has demonstrated the ways the police became powerful, independent political forces in cities and the role of law enforcement in shaping American life beyond crime prevention or control, most notably in maintaining racial, sexual, gender, and class inequality. The history of policing has been separated from the history of crime, taking account of theoretical frameworks that treat crime as a category that is socially and politically constructed over time. Scholarship since the turn of the twenty-first century highlights several themes including the relationship between crisis and reform, the maintenance of social control, the influence of imperialism and empire, and resistance and repression. The history of police and policing in the United States is a vibrant and distinct field on the cutting edge of the carceral turn in American historiography.

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)0.001
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 score0.206
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.035
GPT teacher head0.360
Teacher spread0.325 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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