Relationships among school climate, school safety, and student achievement and well‐being: a review of the literature
Bibliographic record
Abstract
School climate, safety and well‐being of students are important antecedents of academic achievement. However, school members do not necessarily experience school climate in the same way; rather, their subjective perceptions of the environment and personal characteristics influence individual outcomes and behaviours. Therefore, a closer look at the relationship between school climate, safety, well‐being of students and student learning is needed. This review of the literature explores the relationship among school climate, school safety, student academic achievement and student well‐being. Using a systematic review approach, we conducted an overview of empirically based research findings and technical reports that address the following aspects: (a) school climate as a social construct and its connection with school safety; (b) the conditions that contribute to an environment in which students feel safe; (c) the characteristics of particular groups of students who feel unsafe; and (d) the impact of a negative school environment (e.g. a school environment where bullying, victimisation and violence are prevalent) on student achievement, ultimately, secondary school completion and student well‐being. We summarise the state of school climate research, discuss the implications for school policies and practices in the areas of school climate, safety and student success, and provide recommendations for future research.
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 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".