Regulatory Reconstruction of Waste Management to Achieve Efficient and Sustainable Environmental Management
Bibliographic record
Abstract
One of the fundamental problems related to environmental management that continues to receive attention is waste. Likewise, waste management is still a serious problem faced by Pemalang, Central Java. This study seeks to discuss the reconstruction of waste management regulations in Pemalang Regency, Central Java, Indonesia. The results showed that the prevailing regulation is suboptimal and inefficient due to the presence of two concurrent waste management sectors, i.e., public work and environmental department. Besides, the community's low participation at the village level also hinders waste management. Therefore, waste management regulation reconstruction is necessary to address the issues of waste management in Pemalang Regency. The results of the present study highlight the legal aspects and demonstrated the reconstruction of the waste management regulation in Pemalang Regency to obtain sustainable and efficient environmental management. The result of the study showed that waste management could be optimized by focusing on one sector, i.e., the environmental department. In addition, the subdistrict or village government should be given the authority to participate in managing waste, which will create a synergy between the government and the community.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".