Can the Implementation of Conservation Village Increase the Environmental Support in Forest Management in Bukit Barisan Selatan National Park, Lampung, Indonesia?
Bibliographic record
Abstract
This study analyzes the success of implementing a conservation village in obtaining environmental support in forest management in the Bukit Barisan National Park. This research uses a qualitative case study method, reinforced by a desk review of relevant research literature. Interviews and focus group discussions were conducted to gain a deeper understanding of how successful policy implementation was Conservation Village in economic and conservation-based community empowerment in the TNBBS area. The success of policy implementation is reviewed by analysis of marketing policies and further analysis using a matrix of environmental reactions to policy implementation. The study results indicate that marketing policies in terms of policy acceptance and adoption have failed/successful and in terms of strategic readiness, successful with the establishment of a conservation task force tasked with managing and monitoring forest products and implementing this conservation village agreement. The community is an important part of the policy. The village government fully supports being the implementer of the policy. It is manifested in the form of providing access and facilities and for NGOs to provide full support through the provision of budgetary resources and facilitators for training and counseling related to building awareness to save forests.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".