Charting a Greener Future: Collaborative Governance Dynamics in Pekanbaru Sustainable Waste Management
Bibliographic record
Abstract
The centralized policy of local government governance in waste management by applying strict laws and regulations has led to failure.This is evidenced by the increasing trend in landfill waste from year to year.There is a need for improvements and changes to local government policies in governance through the involvement of the private sector and the community.The purpose of this study is to explore the changes in the early stages of two collaboration initiatives: one relying on the traditional collaboration model centered on local government, and the other using a collaboration model involving the private sector and the community in the city of Pekanbaru.This study employs qualitative methods, including 15 indepth interviews with local government officials, private sector representatives, and community leaders, along with an analysis of policy documents and waste management reports from Pekanbaru City.This multi-source approach aims to explore the early-stage shifts from traditional, centralized governance to collaborative waste management involving multiple stakeholders.Key findings suggest that local governments have shifted their role to facilitators, encouraging collaboration with the private sector and community through platforms such as waste banks.The study proposes a new model for collaborative governance in sustainable waste management.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| 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".