Un Legal Instruments on Terrorism Relating to Civil Aviation
Bibliographic record
Abstract
This paper focused on the UN treaties on terrorism that affect civil aviation. The authors analyze all those treaties, studying their scope, structure, and purpose in practice. The treaties have also been studied in a comparative perspective to highlight their strengths and eventual weaknesses, which have been disclosed (uncovered) by the developments in the field of aviation, during their practical application since 1963 until today. The treaties have established several criminal offenses against civil aviation, including the use of commercial aircraft as lethal weapons, then have addressed the issues of jurisdiction between states in concrete cases; extradition; mutual legal assistance between States Parties, etc. As a result, the authors are deeply convinced that the development of the Tokyo-Hague-Montreal-Beijing system, which is part of the whole international legal system of combating terrorism, is fully justified. The new treaties also make this system more coherent and sufficient in relation to preventing, combating, and suppressing unlawful acts against civil aircraft and provide a suitable ground for the development of multilateral international cooperation between state parties and the creation of an effective and uniform legal mechanism. It should be emphasized that the adoption of the Beijing Treaties is part of the implementation of the UN Global Counter-Terrorism.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".