School climate, peer victimization, and academic achievement: Results from a multi-informant study.
Bibliographic record
Abstract
School-level school climate was examined in relation to self-reported peer victimization and teacher-rated academic achievement (grade point average; GPA). Participants included a sample of 1,023 fifth-grade children nested within 50 schools. Associations between peer victimization, school climate, and GPA were examined using multilevel modeling, with school climate as a contextual variable. Boys and girls reported no differences in victimization by their peers, although boys had lower GPAs than girls. Peer victimization was related to lower GPA and to a poorer perception of school climate (individual-level), which was also associated with lower GPA. Results of multilevel analyses revealed that peer victimization was again negatively associated with GPA, and that lower school-level climate was associated with lower GPA. Although no moderating effects of school-level school climate or sex were observed, the relation between peer victimization and GPA remained significant after taking into account (a) school-level climate scores, (b) individual variability in school-climate scores, and (c) several covariates--ethnicity, absenteeism, household income, parental education, percentage of minority students, type of school, and bullying perpetration. These findings underscore the importance of a positive school climate for academic success and viewing school climate as a fundamental collective school outcome. Results also speak to the importance of viewing peer victimization as being harmfully linked to students' academic performance.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".