COUNTERING THE FINANCING OF TERRORISM: LEGAL AND ECONOMIC ASPECTS
Bibliographic record
Abstract
The article analyzes the legal situation regarding the expediency of an early decision to recognize countries that commit armed aggression as terrorist countries. The author outlines the positive aspects and issues that should be worked out in the domestic judicial practice regarding the appeal of persons to judicial institutions with civil and commercial claims against the Russian Federation for compensation for material and moral damages. The article analyzes the current ways and proposals to increase the responsibility of the Russian Federation for the invasion of Ukraine and the large-scale armed aggression of the Russian Federation against democratic Ukraine in order to protect its territorial integrity and national sovereignty. As a result, the authors emphasize that in order to stop the armed aggression of the Russian Federation, the EU Member States need to develop a legal framework for defining states as sponsors of terrorism and states using the means of terrorism, which will entail a number of significant restrictive measures against these countries and will have profound restrictive consequences for the EU's relations with these countries. In this work, the authors outline a number of issues related to the resolution of the judicial immunity of the Russian Federation in the context of the risks of further enforcement of such court decisions. The authors argue that the existing system of regulation of state immunity is not homogeneous in the world, and it is not designed for the situations taking place in Ukraine in connection with the invasion of the Russian Federation, and this raises a number of fundamental questions regarding the application of the concept of immunity to the latter. In addition, to address the issue of regulating relations with countries that show aggression, or rather with countries that act as a terrorist country or a country that finances terrorism, the author cites the experience of the developed algorithms of the United States and Canada as an example. The paper also focuses on the need to develop a procedure for the transfer of frozen Russian assets to Ukraine, since Ukraine needs the seized Russian assets today, primarily for the purchase of necessary weapons to destroy the manifestations of the armed aggression of the Russian Federation, to support Ukrainian forces on the ground, and subsequently to finance large-scale reconstruction of Ukraine. In general, the article presents proposals for the use of economic and legal levers of influence to stop the financial and economic sources of sponsoring military aggression against Ukraine.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".