The incorporation and operation of criminally controlled companies in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it