Confiscation of Property in the Context of Sanctions Policy: Legal Aspects
Bibliographic record
Abstract
Western sanctions regimes show a high degree of coordination. This applies to almost all aspects of the sanctions policy, including approaches to the possibility of confiscating the property of persons subject to blocking sanctions. However, countries that support autonomous sanctions against Russia follow different paths towards the common goal. The emerging approaches to confiscation make it possible to single out two main areas of legal regulation of this issue. In the legislation of the respective country confiscation can be considered either as an instrument of sanctions legislation, or as a measure of responsibility for violating sanctions legislation. Only two countries have so far chosen to use confiscation as an independent instrument of sanctions policy: Ukraine and Canada. Perhaps the United States will join them, but at present, similar to Switzerland, they use confiscation only as part of countering illegal activities. The draft directives developed by the European Commission demonstrate the EU’s commitment not to jeopardize the obligation to protect private property and provide for the possibility of confiscation in exceptional cases as a measure of influence in the fight against criminal activity. Given the importance of protecting private property for a favorable investment climate, it is most likely that the second path will become dominant: asset confiscation will be seen only as a means of responding to violations of the laws of a country that supports autonomous sanctions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".