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Record W2337981493 · doi:10.1177/1088767916634405

The Homicide Drop in Recife, Brazil

2016· article· en· W2337981493 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

VenueHomicide Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsSimon Fraser University
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsHomicideGeographyCriminologyDemographyPoison controlCapital (architecture)Injury preventionDemographic economicsMedicineEnvironmental healthSociologyEconomicsArchaeology

Abstract

fetched live from OpenAlex

Studies in crime concentrations have focused primarily on North America with a rather restrictive set of crime types. In this article, we analyze the crime concentrations and spatial patterns of homicide in Recife, Brazil. Brazil’s homicide rates have remained stable but at high levels, approximately 30 homicides per 100,000. Some places have experienced notable decreases in homicide: In Recife, the capital of Pernambuco, there has been a drop in the homicide rate of 46.67%, 2000 to 2012. We analyzed the decline of homicides finding that it continues to be highly concentrated, but the decrease has not been uniform.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.087
GPT teacher head0.453
Teacher spread0.366 · 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