Incorporating the Illicit: Assessing the Market Supply of Shelf Companies
Bibliographic record
Abstract
Whilst inherently lawful, shelf companies enable the generation of contrived financial structures and arrangements that can facilitate actors to organise their criminal activities and conceal, convert, or control their illicit finances or assets. A stratified market for the purchase and management of such legal structures exists. At the lower end, greater accessibility to shelf companies is enabled by websites offering shell companies, or ready-to-go corporate vehicles, at various prices to anyone interested in setting up a company quickly in a preferred jurisdiction. Although these products have been considered as high-turnover and low-cost, their importance should not be underestimated. They lower barriers to entry to customers interested in concealing, controlling, and/or converting illicit finances domestically and transnationally. To gain insight into the nature and organisation of this market, this article provides a first overview of the online market for shelf companies by empirically analysing data scraped online that identified 296 online shelf company suppliers. The results highlight how some of shelf company suppliers are positioned in a way that is conducive to the facilitation of illicit activities; that there exist a diverse range of suppliers with varying sizes, scopes, and connections; and that the market is global, with a large variety of shelf company jurisdictions provided by suppliers.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".