KEBIJAKAN TRAVEL WARNING AUSTRALIA DAN PENGARUHNYA TERHADAP MINAT KUNJUNGAN WISATAWAN AUSTRALIA DI BALI PERIODE 2002-2015
Bibliographic record
Abstract
Penelitian ini berangkat dari permasalahan terjadinya peningkatan kunjungan \nwisatawan Australia ke Indonesia khususnya Bali meskipun pemerintah Australia \nmasih memberlakukan kebijakan travel warning. Hal ini menjadi problematik ketika \nmelihat fakta bahwa walaupun Australia masih memberlakukan kebijakan travel \nwarning bagi warga negaranya untuk melakukan kunjungan wisata ke Indonesia, \njustru sebaliknya memperlihatkan peningkatan kunjungan wisatawan ke Indonesia. \nOleh karena itu, penelitian ini bersifat deskriptif eksplanatif dengan tujuan mengkaji \ndan menggali berbagai faktor yang berhubungan dengan kebijakan travel warning \nAustralia terhadap Indonesia yang mempengaruhi tingkat kunjungan wisatawannya \nke Indonesia khususnya di Bali. Peneliti beragumen bahwa travel warning \nmerupakan wujud persepsi ancaman tersendiri bagi pemerintah Australia dan \nmasyarakat Australia di Indonesia. Data yang dikumpulkan meliputi data primer dan \ndata sekunder. Data primer dengan wawancara langsung dengan pemangku jabatan \nKonsulat Jenderal Australia di Bali dan Dinas Pariwisata Provinsi Bali, serta \nwisatawan Australia di Bali. Data yang telah terkumpul dianalisis dengan \nmendapakan hasil bahwa masyarakat Australia memiliki opini publik tersendiri yang \nbekembang dalam memandang kebijakan travel warning yang dikeluarkan \npemerintahnya.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".