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Record W2089027148 · doi:10.6000/1929-4409.2013.02.17

From Insult to Injury: How Disputes Begin and Escalate among Adolescents and Young Adults in Medellin, Colombia

2013· article· en· W2089027148 on OpenAlexvenueno aff
Luís Duque, Nilton Montoya

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicGun Ownership and Violence Research
Canadian institutionsnot available
FundersDepartamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)
KeywordsHomicideAggressionBystander effectInsultCriminologyIntervention (counseling)Injury preventionAlcohol consumptionPsychologySuicide preventionPoison controlMedicinePsychiatrySocial psychologyMedical emergencyPolitical scienceLawAlcohol

Abstract

fetched live from OpenAlex

This article aims to contribute to the understanding of circumstances, causes of initiation, and process of escalation of physical disputes or fights resulting in physical injury. We analyzed data from a case-control study of perpetrators of violence between the ages of 15 to 24 (n=373) in the city of Medellín, Colombia. The findings show that 89% of conflicts resulting in injury took place in public places and most often involved males (78%). Six percent involved the consumption of alcohol, 20% reported having used illicit drugs before the initiation of the confrontation. Circa 50% of disputes began because of verbal aggression. Alcohol consumption was found to be associated with verbal aggression towards a friend or companion but not to other circumstances that start disputes. Drug use was not associated with the initiation of disputes. In 18.5% of the cases, a weapon was used while 5% of these disputes ended in a homicide. In none of the cases in which homicide was the outcome was there bystander intervention. In contrast, homicide did not result in the cases in which bystanders intervened.

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.

How this classification was reachedexpand

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.637

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.040
GPT teacher head0.345
Teacher spread0.306 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2013
Admission routes1
Has abstractyes

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