Challenges and Solutions in School Management in Binduriang Sub-District: A Descriptive Qualitative Approach
Bibliographic record
Abstract
This study examines the management of education in a crime-prone area, specifically through a case study in the Binduriang district of Rejang Lebang district of Bengkulu province, Indonesia. This area is known for its high crime rates, which are especially associated with drugs, gambling, theft, and robbery. The study used a descriptive qualitative approach, collecting data through semi-structured interviews and open-ended questionnaires from nine school principals and 10 teachers in the Binduriang subregion. The data analysis used thematic analysis, which included transcription, manual and NVivo coding, categorization, and subject identification to ensure strict pattern recognition and reliable findings. The results show that the main challenges in managing Binduriang education include limited human resources, insufficient financial resources, and a lack of community participation and educational awareness. The study also highlights a number of solutions implemented, such as improving teachers' skills through workshops, and collaborating with security forces and other stakeholders. In addition, the study proposes practical plans to improve the quality of education, including the formation of a strong management team. It also aims to contribute to the understanding and resolution of educational management problems in crime-prone areas, and can serve as a model for other regions facing similar challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".