MétaCan
Menu
Back to cohort
Record W2290147657 · doi:10.1177/0306624x15623282

Social Disorganization and Homicide in Recife, Brazil

2015· article· en· W2290147657 on OpenAlex
Débora V. S. Pereira, Caroline Maria de Miranda Mota, Martin A. Andresen

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

VenueInternational Journal of Offender Therapy and Comparative Criminology · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsHomicideCensusGeographyInequalityCensus tractPopulationDemographic economicsCriminologyPoison controlSocioeconomicsDemographySociologyInjury preventionEnvironmental healthMedicineEconomicsMathematics

Abstract

fetched live from OpenAlex

In this article, we investigate the determinants of homicide in Recife, Brazil, considering social disorganization theory. Using georeferenced homicide data, 2009-2013, and census data, we analyze homicide in Recife using a spatial regression technique that controls for spatial autocorrelation and heteroskedasticity at the census tract level. Overall, we find that homicide in Recife, Brazil, is characterized by social disorganization theory. Specifically, positive relationships are found for inequality, rented houses, and quantity of people, but negative relationships are found for income, literacy, percentage of married people, water supply, public illumination, the percentage of women responsible for the house, and population density. Overall, we find that social disorganization theory provides an instructive framework for understanding homicide in Recife, Brazil. However, there are specific contexts to Brazil that are different from North American contexts.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score0.228

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)0.0000.000

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 head0.543
GPT teacher head0.467
Teacher spread0.076 · 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