A Study Policy Implementation of Waste Management in Konawe Regency-Indonesia
Bibliographic record
Abstract
The purpose of this research is to analyze the policy implementation of waste management in Konawe Regency. The dimension of this research refers to the dimensions of communication, resources, disposition and bureaucracy structure to determine the effectiveness of policy implementation according Edward III (1980). This study also analyzes the obstacles in the implementation of management policy in Konawe District to find out the cause of unsuccessful waste management in Konawe District. Analysis policy implementation of waste management in Konawe Regency using qualitative approach. With the technique of collecting research data using in-depth interview, document study and observation. The result of this research concluded that the policy implementation of waste management in Konawe Regency viewed from communication, resources, disposition and bureaucracy structure aspect has been implemented by Environment Office of Konawe Regency. This study finds that there are still obstacles in the policy implementation of waste management in Konawe Regency is from the aspect of the facility is still lacking, the condition of the work area is very wide, the public awareness is still lacking, the community participation is still lacking, and the public knowledge about waste management is still minimal. The research findings proved that the policy implementation of waste management in Konawe Regency which aims to realize cleanliness, public health and better environmental quality can not be realized as expected, but waste management still needs hard work.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".