DAMPAK PARIWISATA TERHADAP PELUANG USAHA DAN KERJA LUAR PERTANIAN DI DAERAH PESISIR
Bibliographic record
Abstract
The tourism has a very significant role in national economic development. Social changes occured as a result of direct contacts from tourism in tourist areas. One of consequence from the tourism activities is emergence of businesses and employment opportunities which can encourage local economies. The purpose of this research was to identify business and employment opportunities as a result of tourism activities at Pramuka Island and also to identify characteristics of the community. Another purpose is to analyze level of income, linkages between agricultural sector and nonagriculture sector, and transfer of resources (land) that arise due to tourism activities. The research methods are qualitative method which supported by quantitative methods. The results showed that tourism activities in Pramuka island has created business and employment opportunities for local community. Opportunities are predominantly used by natives. Tourism businesses and employment tend to be main livelihood of local people although their income are still at low-income levels. Linkage between agriculture sector and nonagriculture sector in Pramuka Island is shown by the increasing demand in fisheries sector as raw material for some businesses. Transfer of resources tends to occur among natives and there is one policy that prohibits people to build a building around the island ring road. Keywords : impact, tourism, business and employment opportunities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".