Evaluasi dan Arahan Peningkatan Kualitas Infrastruktur Permukiman: Studi Kasus Permukiman Pesisir Perkotaan Kelurahan Buol, Kabupaten Buol
Bibliographic record
Abstract
The development of urban coastal areas presents many challenges, including limited access to basic infrastructure services and vulnerability to natural disasters. This study aims to evaluate the quality of coastal settlement infrastructure in Buol, with a focus on Buol Village in the Biau subdistrict. Buol Village is also the capital of Buol Regency. The results provide recommendations for policy directions on settlement development that are adaptive to the coastal environment. The research method uses a descriptive, quantitative, evaluative case study approach. The study results indicate that the majority of infrastructure facilities are rated "good" with an average score of 3.68. As a result, several other infrastructure components also meet the standards. Basic infrastructure components that meet standards include drainage, waste and sanitation management, and waste management. This research recommends several policy directions, including strengthening residential environmental facilities that are already functioning well, such as education, health, road networks, and electricity, which do not yet meet standards. It is hoped that these will focus on improving services, managing structures, and organizing the environment, like upgrading drainage systems, managing wastewater and sanitation, providing clean water, handling waste, and planning areas better to work with disaster prevention and coastal management. These findings and recommendations aim to support the formulation of more adaptive coastal community development policies by involving community participation and relevant stakeholders to ensure environmental protection in the urban coastal areas of Buol City, while also taking into account the historical background of the area, which in turn will positively impact the region’s image and improve the quality of life of its residents
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".