Implementing Good Environmental Governance to Manage Coastal Abrasion in Bengkalis Regency, Indonesia
Bibliographic record
Abstract
This study aims to investigate the application of the principles of Good Environmental Governance (GEG) in handling beach abrasion in Bengkalis Regency.The research method used is a qualitative phenomenological approach.This research involves collecting in-depth and descriptive data through interviews with relevant stakeholders, such as local governments, environmental institutions, local communities and related experts.A phenomenological approach allows researchers to understand individual views and experiences regarding handling coastal erosion, as well as look for thematic patterns that emerge from the various narratives provided.The collected data was then analyzed using an analysis tool, namely Nvivo 12 Plus.The study results show that implementing Good Environmental Governance (GEG) in handling beach abrasion in Bengkalis Regency has great significance and urgency.GEG principles, such as a strong rule of law, active participation of all stakeholders, access to information, transparency, accountability, decentralization, and justice, provide a comprehensive framework for maintaining coastal environmental sustainability.The principles (GEG) are implemented through cross-sector collaboration, effective coordination between related parties, and increasing stakeholder awareness of environmental interests.Apart from that, the application of risk management is also an integral part of enabling rational and effective decision-making in managing coastal erosion.Barriers such as ineffective coordination, limited resources, low awareness, and conflicts of interest are highlighted, providing insight into the challenges in implementing GEG.The contribution of this research lies in providing an adapted framework for sustainable and equitable coastal management in Bengkalis Regency.Although valuable, this study has limitations, including the regional data focus and interview subjectivity.Future research should broaden the scope and explore socioeconomic impacts and the role of technology in mitigating coastal abrasion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".