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Record W3210288978 · doi:10.1080/17457300.2021.1994615

School violence negative effect on student academic performance: a multilevel analysis

2021· article· en· W3210288978 on OpenAlexaff
Mónica Bravo-Sanzana, Shrikant I. Bangdiwala, Rafael Miranda

Bibliographic record

VenueInternational Journal of Injury Control and Safety Promotion · 2021
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsImpactMcMaster UniversityPopulation Health Research Institute
Fundersnot available
KeywordsMultilevel modelContext (archaeology)PsychologyPoison controlAcademic achievementHuman factors and ergonomicsSuicide preventionInjury preventionInterpersonal communicationSocial psychologyMathematics educationDevelopmental psychologyMedicineEnvironmental healthMathematicsGeography

Abstract

fetched live from OpenAlex

The relative roles of school context and individual student factors are of special interest to educators in measuring academic performance. Little is known about the effect of school violence on a student's academic performance and well-being. The aims of this study were to examine the effects of three types of school violence (direct violence, discrimination, and cyberbullying) on students' academic performance in standardized tests of mathematics, reading and history, and to identify individual student factors that contribute to reducing the negative effect of exposure to violence at school. We used 10th grade Chilean student data from the representative cross-sectional test of the Education Quality Measurement System (SIMCE in Spanish) from 2015. Multilevel linear models, adjusted for gender, incorporated other school and environmental contextual factors, as well as individual student factors. The results show that school violence in its three forms had a negative effect on academic performance. Student self-efficacy, educational expectations and satisfaction with interpersonal relations with their teachers, were important in reducing the negative effect of exposure to violence. The implications for the school are discussed.

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)0.000
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 score0.475
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.010
GPT teacher head0.329
Teacher spread0.319 · 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

Citations18
Published2021
Admission routes1
Has abstractyes

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Same venueInternational Journal of Injury Control and Safety PromotionSame topicBullying, Victimization, and AggressionFrench-language works237,207