Bibliographic record
Abstract
Purpose The language in respect of the money laundering offence of concealing or disguising criminal property is drawn from various international conventions. It is therefore surprising, given the number of jurisdictions, which have incorporated that language into their domestic legislation, that more foreign case law is not used to interpret and properly apply these offences. The purpose of this paper is to rectify that position. Design/methodology/approach This paper uses case law from the USA, where there are frequent money laundering prosecutions, to throw light on the underlying concepts of the concealing or disguising offence and to provide examples of activity which may amount to its commission. Findings The offence of concealing or disguising criminal property is drafted in broad terms. Many of the issues, which have been explored in the US jurisprudence are likely also to arise in criminal proceedings in the UK. Originality/value The paper examines a number of issues, which have not yet been explored by the UK courts. It looks at the approach of the US courts to the differentiation between the mere spending of criminal proceeds and the spending, which is the doing of an act for the purpose of concealing proceeds. It looks at whether concealment must be intended by the defendant's actions or whether it is sufficient if it is an outcome of his actions. It considers how effective any concealment must be and explores the different attributes of criminal proceeds, which may be concealed.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".