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Record W4401857849 · doi:10.1093/icon/moae036

The normative development of laws on asset preservation and confiscation: An examination of emerging best practices

2024· article· en· W4401857849 on OpenAlexaboutno aff
Leonardo S. Borlini, Cecily Rose

Bibliographic record

VenueInternational Journal of Constitutional Law · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicLaw, logistics, and international trade
Canadian institutionsnot available
Fundersnot available
KeywordsConfiscationNormativeAsset (computer security)LawBusinessLaw and economicsPolitical scienceSociologyComputer securityComputer science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.924
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.078
GPT teacher head0.337
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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