Detecting, disrupting and deterring sexual exploitation of trafficked persons: leveraging beneficial ownership registries to reduce criminogenic information asymmetry and raise public expectations
Bibliographic record
Abstract
Beneficial ownership disclosure laws address information asymmetries that enable financial crime. The domain awareness beneficial ownership registries afford enables detection, disruption and deterrence of proceeds of crime from being laundered through corporate vehicles, such as shell and front companies. In Australia and Canada, beneficial ownership regime reforms are now on the political agenda. But how best to balance privacy considerations against the common good in implementation: should beneficial ownership registers be accessible only to government and law enforcement, or also to the public, in whole or in part? The Financial Action Task Force (FATF) and a recent ruling by the European Court of Justice (ECJ) support limited public access. For empirical evidence, this article draws on illicit massage businesses (IMBs), which serve a dual purpose: human trafficking for profit and a seemingly legitimate front to launder illicit proceeds. This article makes the case for robust beneficial ownership disclosure legislation, with basic access for the public and full access for competent authorities, as a necessary yet insufficient condition in confronting financial crime associated with the exploitation of trafficked persons.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".