School violence negative effect on student academic performance: a multilevel analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".