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Record W3047210581 · doi:10.1257/app.20190091

Air Pollution and Criminal Activity: Microgeographic Evidence from Chicago

2021· article· en· W3047210581 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

VenueAmerican Economic Journal Applied Economics · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Conservation and Criminology Analyses
Canadian institutionsUniversity of ManitobaUniversity of Ottawa
Fundersnot available
KeywordsAir pollutionGeolocationPollutionEnvironmental scienceCriminologyPsychologyComputer scienceEcology

Abstract

fetched live from OpenAlex

A growing literature documents that air pollution adversely impacts health, productivity, and cognition. This paper provides the first evidence of a causal link between air pollution and aggressive behavior, as documented by violent crime. Using the geolocation of crimes in Chicago from 2001–2012, we compare crime upwind and downwind of major highways on days when wind blows orthogonally to the road. Consistent with research linking pollution to aggression, we find that air pollution increases violent crime on the downwind sides of interstates. Our results suggest that pollution may reduce welfare and affect behavior through a wider set of channels than previously considered. (JEL K42, Q53)

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.357
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.234
Teacher spread0.213 · 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