Beijing Convention and Beijing Protocol. Changes in the International and Legal Model for Combatting Aviation Terrorism
Bibliographic record
Abstract
The article deals with the issue of the Beijing reform of international criminal aviation law. The author analyses the relevant applicable international law and confronts it with the new legal regulations adopted at the International Civil Aviation Organisation (ICAO) conference in Beijing in 2010. As a result, the author states that the basic change involves the expansion of the catalogue of acts subject to criminalisation as well as the expansion of the circle of persons participating in or supporting actions involving the commission of acts that pose a threat to the safety of civil aviation; the system also specifies the responsibility of collective entities (the so-called ‘Al Qaeda’ clause). The author is deeply convinced that the development of the Tokyo-Hague-Montreal-Beijing system, which is part of the whole international legal system of combatting terrorism, including its financing, is fully justified. The new regulations also make this system more coherent. It is also worth adding that the adoption of the Beijing Convention and the Beijing Protocol is part of the implementation of the Global Counter-Terrorism Strategy adopted by the United Nations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 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".