The normative development of laws on asset preservation and confiscation: An examination of emerging best practices
Bibliographic record
Abstract
Abstract The practice of international asset recovery appears to be in the process of moving beyond the provisions contained in the United Nations Convention against Corruption (UNCAC). These provisions were negotiated twenty years ago, and are now insufficient given the serious contemporary challenges involved in tracing, preserving, confiscating, and returning assets. This article focuses on the limitations of UNCAC’s provisions concerning the preservation and confiscation of foreign assets. These limitations, and the need for progressive development, appear to have been recognized by the UNCAC Review Mechanism, which monitors the implementation of UNCAC by states parties. The Review Mechanism has begun encouraging states parties to adopt “good practices” that go beyond UNCAC’s minimum requirements. In doing so, however, the Review Mechanism has not offered guidance on how exactly states parties ought to go about implementing the best practices that they have identified. The asset recovery laws of Canada, Switzerland, and the United Kingdom demonstrate the need for further consideration of how domestic asset recovery laws ought to be developed. These laws highlight some of the difficult issues raised by more flexible, informal, and rapid forms of international cooperation in the asset recovery context. In particular, they underscore the challenges involved in balancing the general, public interest in combating corruption and recovering stolen assets with respect for and protection of human rights.
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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 | 0.164 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.008 | 0.063 |
| Scholarly communication | 0.029 | 0.018 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.002 | 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".