What school social climate fa ctors affect mathematics performance in secondary school students? A multilevel análisis ( <i>¿Qué factores de Clima Social Escolar afectan el desempeño de Matemática en estudiantes secundarios? Un análisis multinivel</i> )
Bibliographic record
Abstract
The international literature and public policy actions in education have positioned the school environment as a significant component of academic performance and the well-being of the entire educational community. The objective of this study is to identify the school social climate factors that affect secondary school students’ academic performance in mathematics via a multilevel analysis. The model revealed the following factors: Disciplinary Measures, School Violence, Teacher-Student Violence and Encouragement of Classroom Participation. It revealed the teacher’s role in modelling and setting behaviour rules based on respect, equity, justice and inclusion. From this perspective, managing discipline and developing social-emotional competences in associated issues like coping, classroom management, mediation, conflict resolution and positive disciplinary measures should be included in pre-service and in-service teacher training.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".