Pertanggungjawaban hukum atas kecelakaan pesawat penumpang yang disebabkan oleh kecacatan produk (Boeing 737 MAX 8)
Bibliographic record
Abstract
As the time goes by, technology is increasingly developing, in including in aviation world. In this era, people using air transportation to go to their destination. Just like another transportations accidents are things that can be encountered in the air transportation, where a defective product is one of the main factors that cause aircraft accident. Defective product is one of the contributing factors of aircraft accident in aviation transportation. However, these regulations on manufacturer and airlines liability has yet to find an implict direction within the shrines of international convention in aviation field, American Aviation law and Indonesia’s national aviation act. This can be seen from aircraft accidents that involving Boeing 737 MAX 8. The method used in this study is empirical-normative. The data collection method using data from the literature study, analytical methods in the form of descriptive-qualitative analysis of data from exsisting library research. From this study, it is found that passenger can get a compensation from either the manufacture and from the airlines with the principle of strict liabilty as regulated in Montreal Convention of 1999, American Aviation law as FAA Reauthorization Act of 2018, Indonesia’s national aviation act, and Indonesia’s consumer protection act.
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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 | 0.000 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.008 |
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".