Pengaruh Lingkungan Fisik Sekolah Terhadap Perilaku Bullying: Sebuah Tinjauan Sistematis
Bibliographic record
Abstract
Lingkungan fisik sekolah berperan penting dalam membentuk perilaku sosial siswa, termasuk dalam mencegah bullying. Penelitian ini bertujuan mengevaluasi pengaruh desain lingkungan fisik terhadap bullying melalui pendekatan Systematic Literature Review (SLR). Fokus kajian mencakup visibilitas, pengawasan, serta penerapan prinsip Crime Prevention Through Environmental Design (CPTED) dalam desain sekolah. Literatur dikumpulkan dari database seperti ScienceDirect dan Google Scholar, dengan kata kunci “school bullying” pada rentang 2014–2024. Studi yang dipilih merupakan penelitian empiris dalam disiplin ilmu sosial, psikologi, dan kesehatan, yang membahas hubungan desain fisik sekolah dan bullying. Kriteria inklusi mencakup studi yang meneliti desain fisik dengan fokus pada keamanan dan pengawasan, serta dampaknya terhadap pengurangan bullying. Studi non-empiris atau yang tidak relevan dikecualikan. Hasil analisis menunjukkan bahwa prinsip CPTED, seperti peningkatan visibilitas dan pengawasan alami, efektif menurunkan kejadian bullying. Area yang terbuka dan mudah diawasi terbukti lebih aman dibandingkan area tersembunyi. Selain itu, desain yang menunjang kenyamanan siswa turut meningkatkan kesejahteraan psikososial dan mencegah risiko gangguan mental di masa depan. Kajian ini juga menyajikan ilustrasi desain sekolah berbasis CPTED sebagai rekomendasi praktis dalam mendukung kebijakan anti-bullying di lingkungan pendidikan.
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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 | 0.017 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".