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Record W2987777936 · doi:10.1111/ecca.12328

How Do NYPD Officers Respond to Terror Threats?

2019· article· en· W2987777936 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEconomica · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsQueen's University
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Michigan
KeywordsPolice departmentHomeland securityAl qaedaTerrorismCriminologyLaw enforcementBaseline (sea)Political scienceLawSociology

Abstract

fetched live from OpenAlex

Using data from the Stop‐and‐Frisk programme of the New York Police Department (NYPD), we evaluate the impact of a specific terrorist attack threat from Al Qaeda on policing behaviour in New York City. We find that after the Department of Homeland Security raised the alert level in response to this threat, people categorized as ‘Other’ by the NYPD, including Arabs, were significantly more likely to be frisked and have force used against them, yet were not more likely to be arrested. These individuals were in turn less likely to be frisked or have force used against them immediately after the alert level returned to its baseline level. Further, evidence suggests that these impacts were larger in magnitude in police precincts that have higher concentrations of mosques. Our results are consistent with profiling by police officers leading to low‐productivity stops, but we cannot rule out that it constitutes efficient policing given important differences between deterrence of terrorism versus other crimes.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.805
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0030.006

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 head0.049
GPT teacher head0.349
Teacher spread0.300 · 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