“Effectiveness of the 2010 Beijing Convention and Protocol \nin Addressing Aviation Security Threats \n
Bibliographic record
Abstract
Following the September 11 terrorist attacks in America and the occurrence of other \nnew and emerging threats to civil aviation, the international community has been \nfaced with the harsh reality in the field of aviation security, hence, several legal \nattempts were made to address such aviation threats. Consequently, the International \nCivil Aviation Organization (ICAO) in its 37th Diplomatic Conference of 2010 held \nin Beijing, China, adopted two new international legal instruments namely; \nConvention on the Suppression of Unlawful Acts Relating to International Civil \nAviation (Beijing Convention of 2010) and Protocol Supplementary to the Convention \nfor Suppression of Unlawful Seizure of Aircraft (Beijing Protocol of 2010). This study \nreviews the historical background to the development of international air law on \naviation security and further assesses and analyzes in detail the provisions of the \nBeijing legal instruments in comparison with the previous treaties. Finally, the key \nquestion of whether or not the new Beijing Convention and the Protocol are adequate \nand effective in combating threats to aviation security is addressed through doctrinal \nresearch. In this regard, information was collected from various primary and \nsecondary sources of law including books, articles, conventions, protocols, statutes \nand internets. Further information was gathered from various aviation stakeholders \nincluding aviation experts, security staff, lawyers, passengers, ground handlers, crews, \nregulators, airline and airport operators. From the information collected, the study \ncomes up with conclusion, observations and recommendations which may be useful in \naddressing new and emerging threats to aviation security. \n
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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".