Criminal Wealth Law: From Maple Syrup to Hells Angels and Unexplained Wealth Orders
Bibliographic record
Abstract
A significant dimension of modern crime management focuses on property, wealth, money, and financial activity. The organizing theme of late twentieth-century products such as anti-money laundering laws, criminal confiscation, criminal forfeiture, and civil forfeiture is detecting and capturing wealth associated with criminal activity. Such products, which have proliferated since the inception of modern management in the late 1980s, might be described as criminal wealth law. An area of acute contemporary interest, a trilogy of recent developments, might be said to mark a certain sharpening of the edges of Canadian criminal wealth law. The first, a Supreme Court of Canada decision involving the theft of maple syrup, hones federal criminal forfeiture machinery, the principal anti-criminal wealth device. The second, a British Columbia Court of Appeal decision about clubhouses owned by the Hells Angels, sharpens a provincial criminal wealth device, civil forfeiture law. The third, arguably the most substantively significant of the trilogy, introduces a new tool to a province’s wealth-focused toolkit, an unexplained wealth order regime. This essay examines this trilogy of contributions to criminal wealth law.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.037 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".