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Record W80523290

Impact of law enforcement activities on risk of firearm deaths in Brazil

2006· article· en· W80523290 on OpenAlex
K.H. Tiedemann

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 conference on Modelling and simulation · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsBC Hydro (Canada)
Fundersnot available
KeywordsLaw enforcementOrdinary least squaresEnforcementDrug traffickingDe factoGeographyCriminologyPolitical scienceEconomicsLawSociologyEconometrics
DOInot available

Abstract

fetched live from OpenAlex

Over the past decade, Brazil has become one of the most violent societies in the world, with death rates from firearms regularly exceeded only in Columbia and El Salvador. In some slums or favelas, drug gangs have become the de facto authorities, and police activity is limited to sporadic raids with only limited ongoing presence. The purpose of this study is to provide an analysis of the determinants of firearm deaths in Brazil. This study uses cross-section analysis of state level data to model the impact of the costs and benefits of crime to criminals on firearm deaths in Brazil. Both ordinary least squares and two-stage least squares models are estimated, with the latter allowing for the possibility that the explanatory variables are determined within the model rather than being strictly exogenous.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.957

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 head0.101
GPT teacher head0.422
Teacher spread0.321 · 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