Illegal, Unless: Freezing the Assets of Russia’s Central Bank
Bibliographic record
Abstract
Abstract In response to the Russian invasion of Ukraine on 22 February 2022, the European Union (EU) and states such as Canada, Japan, Switzerland, the United Kingdom and the United States of America froze assets of the Russian central bank held in their jurisdictions. The sanctions fit in a longer pattern of states freezing assets of foreign central banks, which has been criticized by several states to be incompatible with the law of state immunity. The criticism on these types of sanctions raises the question whether freezing assets of Russia’s central bank complies with the law of state immunity. This article answers this question by investigating whether the law of state immunity is confined to the jurisdiction of courts or if it also applies in the context of executive action. Considering that the law of state immunity also applies to executive action, these sanctioning states (and the EU) violated Russia’s state immunity by freezing assets of Russia’s central bank. These sanctions, however, could be justified as (third party) countermeasures in response to Russia’s invasion of 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".