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Record W2943300260 · doi:10.5539/ies.v12n5p17

Barras Bravas: Youth Violence in Football Crowds at School

2019· article· en· W2943300260 on OpenAlexvenueno aff
José Javier Bermúdez Aponte, John A. Buitrago-Medina, Bibiana Ávila-Martínez, Abel J. Ortiz-Mora

Bibliographic record

VenueInternational Education Studies · 2019
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)TriangulationFootballCrowdsQualitative researchSupporterSociologyMathematics educationPublic relationsPsychologyPolitical scienceSocial scienceGeographyComputer scienceComputer security

Abstract

fetched live from OpenAlex

This article results from the analysis of the phenomenon of barras bravas (violent supporter groups) in football and its influence in school coexistence at three public educational institutions in Bogotá. The methodology of the study was mixed with a concurrent triangulation design (DITRIAC), hence diverse instruments were employed to verify the findings and cross-validate quantitative and qualitative data. The information obtained from a survey applied to 300 students was complemented with life histories, field notes and a document review of the institutional reports on school coexistence. The study revealed that violence emerges as a consequence of the participation in barras bravas, whose members attend the institutions where this research was conducted. The discussion reflects how important it is to vindicate the role of the school within the framework of public policies which both integrate youth dynamics and articulate programs and projects suitable for the Colombian context.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.060
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0070.003
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.041
GPT teacher head0.381
Teacher spread0.341 · 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 source (direct Gemma or distilled Codex), 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
Published2019
Admission routes1
Has abstractyes

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