Increasing the Role of Local Communities in Protecting Turtles on Liukang Loe Tourist Island: Maritime Diplomacy Policy of the Local Government
Bibliographic record
Abstract
This research examines the role of local communities in tourism development and their involvement in economic cooperation on Liukang Loe Island, Bulukumba Regency, South Sulawesi. One example of the local community’s contribution is the management of a turtle hatchery as a tourist destination, which is entirely managed by the local community. Shows that support and active participation from local communities are crucial for enhancing the economy and ensuring tourism sustainability. Furthermore, this research highlights the international cooperation between Bulukumba Regency and Canada in spice production, demonstrating local community involvement in supporting economic development. This study uses a qualitative approach through interviews with several local community stakeholders. The findings show that local community involvement positively impacts community income and strengthens the region’s strategic role in the global economic cooperation network. These findings confirm that local community participation is essential in creating sustainable tourism and supporting regional economic development and international cooperation. On Liukang Loe Island, engagement in maritime diplomacy is still limited because the tourism development and the turtle hatchery still need to be connected to global networks. Indonesia’s maritime, as outlined in the Djuanda Declaration, emphasizes the importance of integrating maritime economic development and marine.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".