HARMONISASI UNDANG-UNDANG NOMOR 10 TAHUN 2009TENTANG KEPARIWISATAAN DENGAN PRAKTIK PERDAGANGANINTERNASIONAL DI BIDANG JASA PARIWISATA DI INDONESIA
Bibliographic record
Abstract
Indonesia has big potency in tourism. This, is admitted not only by our tourist observers but also by other parties abroad. While tourism is being developed at the same time we face liberalization on trade in services. And tourism is one of the sector entered in to liberalization. This research combines both library and field research. Law number 10/2009 and GATS-WTO completed with other literatures become pure legal premise, proceeded with field research. Field research is needed to see not only law in concretto, but also what ideal is. Globalization/liberalization with their regulations gives opportunities and challenges at the same time. And liberalization in a country where massive agrarian remain and rural area scatters are something not easy, moreover trade liberalization in services i.e. tourism. Law no. 9/2009 is to respond the above mentioned liberalization. The law is effective and become a base of tourist activities and shall be able to perform its usefulness. Indonesia is rich in natural wealth with its potency for tourism, this should be transformed in to enactment of a law. Law No. 10/2009 vis-a-vis GATS regulations which is part of the three legs of WTO�s liberalization.other legs are GATT and TRIPS. Stipulations which are conformed between two regulations are found, besides there are stipulations which may negotiated in the next future for the sake of justice
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.070 | 0.016 |
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".