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Review of the Characteristics of Boys and Girls Involved in School Bullying

2023· article· en· W4382704513 on OpenAlexaff
Victoria Maria Ribeiro Lembo, Manoel Antônio dos Santos, Manuella Cana Brasil Feijó, André Luiz Monezi Andrade, Marcela Almeida Zequinão, Wanderlei Abadio de Oliveira

Bibliographic record

VenuePsicologia - Teoria e Prática · 2023
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsChild, Adolescent and Family Mental Health
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsPsychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

Bullying at school affects the health and development of children and adolescents. This study aimed to describe evidence about the characteristics of boys and girls identified as bullies in bullying situations. This is a review with publications from 2015 to 2020, operationalized in SciELO, Scopus, Web of Science, and PsycINFO. Data were independently extracted by two reviewers and submitted to methodological quality assessment. The corpus was composed of 27 primary studies. It was found that boys do more bullying and are more engaged in physical aggression. Girls engage more in verbal or psychological type violence. Social/cultural issues are factors consistently associated with such differences. Regarding the consequences of the aggression perpetrated, bullies, regardless of gender, may also have mental health problems. This study gathers quantitative evidence on characteristics of boys and girls who engage in bullying at school. Differences/similarities should be considered in anti-bullying intervention programs.

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)0.001
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 score0.040
Threshold uncertainty score0.707

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.312
Teacher spread0.279 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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