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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".