UNDERSTANDING URBAN GOVERNANCE IN THE CONTEXT OF PUBLIC-PRIVATE PARTNERSHIPS: A Case Study of Solid- Waste Management in Rayong Municipality, Thailand
Bibliographic record
Abstract
Local government practices in Thailand have become more networking orgovernance-oriented since the promulgation of the Constitution of 1997 and the Decentralization Plan and Process Act of 1999. Several local governments have applied modern concepts of New Public Management (NPM) in order to perform their tasks. Public- Private Partnership is, therefore, regarded as a mode of governance for the sake of successful public service delivery. This article aims to describe and analyze local governance in political economy perspective. The case study of Rayong Municipality is selected to present the factors that drove the emergence of public-private partnership and how local government coalitions cooperate in public service delivery, especially the case of solid-waste management. The waste problem in Rayong Municipality had risen considerably due to the rapid increase in population, a trend that may continue in the future. The causes of the problem are many; lack of proper disposal units, limited budget, personnel and landfill areas. This problem has a negative impact on the quality of life in the municipality and therefore this is best dealt with collectively. The project that has been implemented is the waste recycling scheme, garbage banking in schools and communities. The Waste-to-Fertiliser and Energy project makes the Integrated Waste Management Approach complete with the cooperation from other government agencies and NGOs and the involvement of the private sector as PPP.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".