Disposition of Waste Management Policy Implementers Through the Regional Cooperation Scheme
Bibliographic record
Abstract
This study departs from the waste management problem through inter-regional cooperation policies represented by North Sumatra Province, Medan City, Binjai City, Deli Serdang Regency, and Karo Regency. This study aims to describe the root causes that influence the low disposition of inter-regional cooperation policy implementers in waste management in the Mebidangro area. This study used a qualitative research method with a descriptive approach. Primary data was obtained from in-depth interviews with stakeholders, and field observations were made to obtain data and information. The research findings show that the attitude of policy implementers shows a negative response to waste management in the Mebidangro area. This condition is motivated by a lack of knowledge, differences in perspectives, separate management, differences in spatial planning, and a lack of trust, giving rise to suspicions that only one party will benefit. It is very important to build a model of inter-regional cooperation that does not consider hierarchical networks to eliminate sectoral egos and form a favorable attitude from each actor towards policy implementation. Future research needs to replicate this research by looking at the impact of the planned cooperation scheme in the national strategic area in waste management, both from social, technological, economic, and cultural aspects.
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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 | 0.013 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".