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Street Stops and Broken Windows Revisited: The Demography and Logic of Proactive Policing in a Safe and Changing City

2009· article· en· W1599666175 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueeYLS (Yale Law School) · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsInterdictionCriminologySocioeconomic statusEnforcementOrder (exchange)Law enforcementPolitical scienceDemographic economicsGeographyDemographySociologyPopulationBusinessEconomicsLaw

Abstract

fetched live from OpenAlex

This chapter examines the development of “order maintenance policing” in New York City. It studies the stop-and-frisk activities of New York City police officers by examining temporal and spatial patterns of stops from 1999, 2003, and 2006. Findings reveal that stop rates have increased by 500 percent since 1999 despite little change in crime rates Stop activity was greatest in poor and minority communities, and stop patterns were more closely tied to demographic and social conditions than to disorder or crime. The efficiency of stops, measured as “hit rates,” dropped considerably, with the sharpest declines occurring in minority neighborhoods. Overall, the findings illustrate that the racial-spatial concentration of excess stop activity threatens to undermine police legitimacy and diminish the social good of policing, while doing little to reduce crime or disorder.

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.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.028
GPT teacher head0.312
Teacher spread0.284 · 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