Drone as a Target of Terrorist Attack and a Weapon Against Terrorism – Analysis in the Light of International Law
Bibliographic record
Abstract
Abstract Unmanned aircraft vehicles (or “UAVs”) have became a symbol of modernity and development of aviation, both in its civil and military sector. Stronger connection of UAVs with air industry means not only many advantages for air travellers and military forces, but also potential involvement in terrorism. Unmanned aircraft serving civil air transport can become target of terrorist attack. On the other hand, such a device can also be used as a handful weapon in fight against terrorism. The aim of hereby article is to study both such aspects of usage of UAVs from legal perspective in order to answer a question whether provisions of international law currently being in force accurately reflect the reality of fight against terrorism. For that purpose, applied is research based on a method of analysis of relevant legal acts (conventions forming Tokyo-Hague-Montreal-Beijing system and documents related to international humanitarian law) and critical commentary thereto, enriched with practical review of real and current cases involving unmanned aircraft vehicles.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".