Confiscating Russia’s Frozen Central Bank Assets: A Permissible Third-Party Countermeasure?
Bibliographic record
Abstract
Abstract The war of aggression by a permanent member of the Security Council, combined with the availability of its assets on the territory of other states, creates an opportunity to solve one of international law’s enigmas: the legality of third-party countermeasures in the general interest. Would confiscating Russia’s frozen Central Bank assets and making the proceeds available to repair the war damage in Ukraine be permissible as such a countermeasure? This paper argues that state immunity cannot be relied upon to prevent the freezing or confiscation of foreign central bank assets by direct executive action; that freezing foreign state assets is permissible as a third-party countermeasure to stop a serious case of aggression; and that confiscation would not qualify as a countermeasure but may be permissible as a ‘lawful measure’ to repair the damage. Recent changes in Canadian legislation support the existence of such a permissive rule. On the other hand, controversial measures by the United States to control the assets of the Afghan Central Bank demonstrate the need for safeguards against abuse.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".