Management of Maritime Tourism of the Kei Indigenous Peoples of Southeast Maluku Regency as an Economic Driver Based on Environmental Sustainability
Bibliographic record
Abstract
The development of marine tourism in the Kei community of Southeast Maluku Regency has a very important role both in terms of economic law and environmental law. In terms of economic law, the development of marine tourism plays a role in increasing the country's foreign exchange income and improving the economy of the Kei people of Southeast Maluku Regency. This research was conducted using an empirical juridical approach which is a descriptive qualitative analysis research. This study tries to describe what happens in the management of marine tourism in the Kei Indigenous community as an environmentally friendly economic driver based on environmental sustainability. The answers found from this research are: 1. Factors that affect environmental damage caused by: a. anthropogenic (human activities), b. non-anthropogenic (ecological changes, natural factors), c. Awareness of people living around marine tourism areas in Southeast Maluku Regency. 2. The factors that influence the level of community income in marine tourism locations are business capital variables that have a strong or significant effect on people's income in Kei Indigenous Maritime Tourism, Southeast Maluku Regency. In addition to the factors above, there are also several influencing factors, namely: 1) The Effect of Business Length on Community Income on Marine Tourism 2) The Effect of Education Level, 3 The Effect of the Number of Visitors.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".