Policy Strategy for Sustainable Management of Mangrove Ecotourism in Siak Regency, Riau Province, Indonesia
Bibliographic record
Abstract
Siak regency, Indonesia, has more than two hundred-thousand-hectare mangrove areas. Local community for all this time has been developed mangrove areas as ecotourism, but they still lack sustainable management to maintain it. Therefore, this study aims to promote strategies' policy for the sustainable management of mangrove ecotourism in the Siak regency. The research was conducted from July 2019 to July 2020 at 3 locations of mangrove ecotourism, which involved 30 respondents, consisting of 21 people to assess the SWOT analysis and 9 experts to assess AHP. For generating the sustainable policy strategy, A'WOT, a combination of AHP (Analytical Hierarchy Process) and SWOT (Strengths, Weaknesses, Opportunities, and Threats), was applied. The SWOT data in mangrove ecotourism management includes strengths (2.797), weaknesses (-0.22), opportunities (3.668), and threats (0.149). The results showed that there were six policies needed to excuse. From these policies, the opening opportunities for investors for ecotourism development policy (0.243), improving facilities and infrastructure policy (0.194), providing business training on ecotourism policy (0.178), increasing ecotourism promotion policy (0.111), establishing cooperation between government and stakeholders policy (0.97), and maintaining mangrove ecosystem policy (0.79). It was concluded that mangrove ecotourism in Siak regency required priority funding to develop facilities and ecotourism's business training to cultivate sustainable management of mangrove ecotourism. Corporation between the government and stakeholders was needed to accelerate the realization of the policies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".