IMPLEMENTASI KONVENSI MONTREAL 1999 \nDALAM REGULASI PENERBANGAN DI INDONESIA
Bibliographic record
Abstract
Menghadapi kebutuhan akan penerbangan internasional yang semakin \nmeningkat, pembentukan Konvensi Montreal 1999 dipandang sebagai langkah \nawal dalam memperbaiki aktivitas dalam dunia penerbangan khususnya dalam \nbidang regulasi. Tidak hanya melakukan pembaharuan pengaturan mengenai \npermasalahan ganti rugi dan yurisdiksi dalam konvensi - konvensi terdahulu, \nKonvensi Montreal 1999 berhasil menciptakan sebuah unifikasi hukum yang \nsebelumnya tidak berhasil dicapai. Hingga tahun 2003 tercatat 23 negara telah \nmenjadi peserta dari konvensi ini. \nPada tahun 2016, Indonesia akhirnya menjadi salah satu negara peserta dari \nKonvensi Montreal 1999. Namun dalam pemberlakuan konvensi tersebut, ternyata \nmenimbulkan konflik hukum antara pengaturan yang ada dalam Konvensi \nMontreal 1999 dengan regulasi mengenai penerbangan di Indonesia. Menghadapi \nsituasi tersebut, apabila Indonesia hendak sungguh - sungguh memberlakukan \nKonvensi Montreal 1999 ke dalam hukum nasionalnya, maka Indonesia \nmemerlukan penyesuaian tehadap pengaturan yang ada agar permasalahan \nmengenai konflik hukum tersebut dapat teratasi.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.066 | 0.015 |
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".