Development and Validation of a Model Predicting Students’ Sense of School Belonging and Engagement as a Function of School Climate
Bibliographic record
Abstract
For many years, studies have explored the relationship between school belonging and engagement, two concepts that are associated with several positive outcomes. However, the relative influence that school climate may have on these components has received little attention. Based on the theoretical perspective of Janosz et al (1998), school belonging and engagement were examined as a function of multiple dimensions of school climate, and were tested across genders. The research took place in Morocco, and participants were 238 students from 9th grade (101 males, 137 females; Mage = 15.1) living in the cities of Casablanca and Témara. Students completed a questionnaire aimed at measuring school belonging, school engagement, and school climate. Correlational and structural equation modeling methods were used to analyze the aforementioned relationships. Results showed that only the climate of justice had a positive effect on school belonging, which, in turn, had a positive effect on the three types of school engagement. The multigroup analysis revealed the relation between school belonging and behavioral engagement to be partially invariant across genders. These results highlight the benefits of creating a positive school climate which can support students' belonging and engagement.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".