The incorporation and operation of criminally controlled companies in Canada
Bibliographic record
Abstract
Examines how financial proceeds of entrepreneurial crime are disbursed throughout Canada’s legitimate economy, focusing on the use of criminally controlled companies as money laundering vehicles. Outlines the design of the research, including data sources, sampling method, data collection, and limitations of the data; the main source of primary data are the proceeds of crime cases taken from the files of the Royal Canadian Mounted Police. Discusses the findings: drug trafficking is the largest single source of criminal proceeds. Moves on to the criminal companies involved: these have a long history in North America, and while they exist for various reasons, money laundering is one of their main functions. Details a case study, that of Gary Hendin, an Ontario lawyer who laundered around CDN12 million in drug money during the late 1970s and early 1980s. Indicates the types of companies used and their methods for laundering money: nominees as owners or directors, a company hierarchy, fake loans or investments, selling a company, buying a company already owned by a criminal enterprise, fictitious business expenses and false invoices, fictitious salaries, and offering shares in a public company.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".