Bibliographic record
Abstract
II. The Montreal ConventionThe Montreal Convention for the Suppression of Unlawful Acts against the Safety of Civil Aviation of 19712 is the third convention in an international terrorism regime that has evolved piecemeal, usually as a response to particular terrorist activities.3 So, the first concern was with offences against civil aviation, originally in the form of hijacking 4 and then in the form of sabotage of aircraft 5 and airports.6 This rdgime was later extended to ships 7 and offshore installations.8 When the international community became apprehensive about attacks on diplomatic and international personnel, two further international conventions were adopted, dealing with internationally protected persons, essentially diplomats, and with the taking of hostages.0 The transportation of nuclear material has been the subject of a separate agreement, as has the "marking" of plastic explosives for the purpose of detection.12 More recently, the international 2 Montreal Convention for the Suppression of Unlawful Acts against the Safety of Civil Aviation, Sept. 23, 1971, 24 U.S.T. 564, 974 U.N.T.S. 177 [hereinafter Montreal Convention].3 For example, the Montreal Convention was adopted as a response to the bombings of three aircraft in September 1970, two hijacked to Dawson's Field in Jordan and exploded on the ground, a third bombed on the ground at Cairo. Tokyo Convention on Offences and Certain other Acts Committed on Board Aircraft,
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.013 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.064 | 0.005 |
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".