MétaCan
Menu
Back to cohort

Confiscation of Property in the Context of Sanctions Policy: Legal Aspects

2023· article· en· W4383219759 on OpenAlexaboutno aff
V. Yu. Slepak

Bibliographic record

VenueActual Problems of Russian Law · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Sanctions and International Relations
Canadian institutionsnot available
Fundersnot available
KeywordsConfiscationSanctionsLegislationBusinessPrivate propertyContext (archaeology)Law and economicsLawPolitical scienceEconomics

Abstract

fetched live from OpenAlex

Western sanctions regimes show a high degree of coordination. This applies to almost all aspects of the sanctions policy, including approaches to the possibility of confiscating the property of persons subject to blocking sanctions. However, countries that support autonomous sanctions against Russia follow different paths towards the common goal. The emerging approaches to confiscation make it possible to single out two main areas of legal regulation of this issue. In the legislation of the respective country confiscation can be considered either as an instrument of sanctions legislation, or as a measure of responsibility for violating sanctions legislation. Only two countries have so far chosen to use confiscation as an independent instrument of sanctions policy: Ukraine and Canada. Perhaps the United States will join them, but at present, similar to Switzerland, they use confiscation only as part of countering illegal activities. The draft directives developed by the European Commission demonstrate the EU’s commitment not to jeopardize the obligation to protect private property and provide for the possibility of confiscation in exceptional cases as a measure of influence in the fight against criminal activity. Given the importance of protecting private property for a favorable investment climate, it is most likely that the second path will become dominant: asset confiscation will be seen only as a means of responding to violations of the laws of a country that supports autonomous sanctions.

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)0.000
metaresearch head score (Gemma)0.000
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.777
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.041
GPT teacher head0.246
Teacher spread0.205 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueActual Problems of Russian LawSame topicEconomic Sanctions and International RelationsFrench-language works237,207