Carpe Pecuniam: Criminal forfeiture of tainted legal fees
Bibliographic record
Abstract
A person charged with money laundering has a right to legal \nrepresentation and a lawyer is entitled to defend such person. What if the \nlawyer is paid with dirty money? This paper explores the legal status of \ntainted fees, to determine whether such moneys should be forfeitable and, \nif so, what forfeiture means for the client’s right to legal representation and \nthe lawyer’s right to practise his\\her profession. This is an issue of \ninternational import and the paper considers criminal forfeiture of tainted \nlegal fees in South Africa, the USA and Canada. All three jurisdictions \nprovide for the criminalisation of tainted fees. However, South African \nlawyers are most in peril both of prosecution and conviction for accepting \ntainted fees and of having such fees confiscated. Whereas the USA and \nCanada uphold the right of lawyers to practise their profession, South \nAfrica appears to negate it. The South African position requires reform.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".