KARAKTERISTIK DAN MOTIVASI WISATAWAN EKOWISATA DI BALI (STUDI KASUS DI JARINGAN EKOWISATA DESA)
Bibliographic record
Abstract
This study discusses about tourist characteristic and motivation in Pelaga, Badung Regency, Sibetan, Karangasem Regency, and Tenganan, Karangasem Regency. These three villages were developed into ecotourism village by JED (Village Ecotourism Network). Ecotourism is a community- based tourism, enviromentally sound, and responsible for sustainability. By seeing the number of visitor in Pelaga Ecotourism Village which has yet to reach the target, this is the impact of marketing system is still very common conducted without regard to the characteristics and motivations of tourists. This research purposes is to know the tourist characteristic and motivation who visit Pelaga, Sibetan, and Tenganan Ecotourism Village. Data collection in this research is done by direct obeservation to Pelaga Village, Sibetan Village, and Tenganan Village. Deep interview with the manager of JED and then deep interview with the coordinator of JED in every village, and also deep interview with the tourist to know their motivation visit Pelaga Ecotourism Village. While also using literature study and documentation. The result of this research show that in term geographic characteristic the visitor in Pelaga, Sibetan, and Tenganan Village is come from various country namely USA , Australia, Thailand, Japan, Germany, Canada, Netherland, England, France, Norway, Belgium, Philippines, Italy, Singapore, Malaysia, Cambodia, China, Poland, East Timor, Finland, Korea. In term socio- demographic characteristic the tourist who visit Pelaga and Sibetan dominated by man and in productive age, while in Tenganan is dominated by women and in older age. The whole tourist in three villages are work in private or public sector, and high educational background. Most of tourists who visit, have the motivation to know the culture in three villages.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".