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Record W3104126379 · doi:10.6000/1929-4409.2020.09.89

Latin American Production on Gender Violence on Scopus, 2010 -2019

2020· article· en· W3104126379 on OpenAlexvenueno aff
Ronald M. Hernández, Miguel A. Saavedra-López, Xiomara M. Calle-Ramírez, Julio Cjuno, Fernando Escobedo

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicVarious Academic Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLatin AmericansScopusInterpersonal violenceDomestic violencePolitical scienceLibrary sciencePoison controlSuicide preventionMedicineEnvironmental healthMEDLINELaw

Abstract

fetched live from OpenAlex

The study describes the characteristics of publications on gender violence written by authors affiliated with Latin American institutions, in journals indexed to Scopus during the period 2010-2019. A descriptive and retrospective analysis of 2,568 articles is carried out. Latin American scientific production represents 5.3% of world production. Brazil is the country with the highest production, followed by Mexico and Chile. Latin American scientific production has been published in 572 journals. Ciencia e Saude Coletiva (Brazil) is the journal with the largest number of publications, followed by Cadernos de Saude Publica (Brazil) and Journal of Interpersonal Violence (United States). besides, the authors are mainly affiliated with the Universidade de Sao Paulo - USP, followed by the Fundacao Oswaldo Cruz Universidade Federal do Rio Grande do Sul. Finally, the keywords, domestic violence, intimate partner violence and gender violence present an increasing trend of studies since 2016. Therefore, it is necessary to strengthen and stimulate the generation and dissemination of scientific studies by Latin American researchers.

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)0.006
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.994
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.028
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0950.120
Science and technology studies0.0010.001
Scholarly communication0.0080.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0200.006

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 head0.147
GPT teacher head0.416
Teacher spread0.269 · 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.

Study designObservational
DomainEvaluation
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
Published2020
Admission routes1
Has abstractyes

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