Green Economy Practices and Sustainability Achievements in Environmental Governance:
Bibliographic record
Abstract
The green economy is an economic approach that emphasizes sustainable growth through environmentally friendly program strategies. Bukit Peramun tourist village in Belitung is a prime example of a green economy initiative in the tourism sector, positively impacting local community's economy through its management practices. This research aims to explore how the implementation of green economy strategies has the potential to enhance the welfare and empowerment of the community in developing their area as a tourist attraction. Employing a qualitative method, this study gathers data through secondary sources, observations, and interviews with local tourism offices, marketing departments, and creative economy stakeholders. The findings indicate that Bukit Peramun has emerged as a source of new economic opportunities, with its management effectively utilizing the green economy concept to promote community empowerment as active participant in development. The emphasis on local wisdom serves as a compelling draw for tourists interested in cultural experiences, fostering a sense of pride among residents and creating economic opportunities tied to the village's unique offerings. Furthermore, supports from collaborative networks with policymakers facilitates access to essential services, enhancing the development of Bukit Peramun as it operates within the green economy framework.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".