Working Paper 42: From sanctions to confiscation while upholding the rule of law
Bibliographic record
Abstract
Written in the light of Russia's war of aggression in Ukraine, the Working Paper explores whether it is justifiable to confiscate assets frozen under financial sanctions in order to redirect them to the victims of state aggression. The paper first explores the concept of sanctions and financial sanctions (asset freezes) and what they mean in practice. Using the example of Canada, which has introduced a legislative mechanism for this purpose, the paper analyses whether states should be able to confiscate sanctioned assets purely on the basis that they have been sanctioned. It then looks at more established measures that states could adopt and apply to target sanctioned assets, including: Traditional conviction based confiscation measures, including 'extended confiscation' mechanisms Non-conviction based confiscation (forfeiture) measures Unexplained wealth laws The paper recommends ways to maximise the effectiveness of these alternative avenues for recovering assets, which are much less controversial and can arguably be applied without infringing on legal rights. Opting for mechanisms that abide by established legal rights will not only significantly increase the chance of recovering assets without subsequent legal challenges. It will also ensure that the very reason for targeting the assets in the first place – namely to seek justice and compensation for acts of aggression – is not undermined through the erosion of the rule of law.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".