Building a Sustainable Mining Governance Model Through Decentralization Performance and Governance Transformation in Indonesia
Bibliographic record
Abstract
This study examines the impact of environmental quality, governance, society, and the economy on decentralization performance and sustainable mineral and coal mining governance in Indonesia.Effective governance in the mining sector is crucial for ensuring sustainability, yet challenges remain in balancing economic growth, environmental protection, and community welfare.The research aims to assess the mediating role of decentralization performance in fostering sustainable mining governance and the moderating effects of governance transformation, including policy reforms, institutional changes, and regulatory updates.A quantitative approach is applied using Structural Equation Modeling (SEM) with the WarpPLS method to analyze governance mechanisms in mineral and coal policy.The results indicate that environmental quality, governance quality, community quality, and economic quality significantly influence decentralization performance.Additionally, decentralization performance enhances sustainable mining governance, reinforcing the need for an effective governance structure.Governance transformation strengthens these relationships, improving regulatory frameworks and institutional mechanisms to support sustainability.This study highlights the importance of structured decentralization and governance reforms in ensuring responsible mining practices.The findings contribute to policy discussions by providing insights into enhancing governance mechanisms for sustainable resource management.Strengthening decentralization and governance transformation can lead to more balanced and effective mining governance in Indonesia.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".