Putting an end to snow-washing: The case for a publicly accessible corporate registry of beneficial owners in Canada
Bibliographic record
Abstract
The term ’snow-washing’ was discovered by Toronto Star and CBC journalists investigating the 2016 Panama Papers. The term allegedly comes from the intermediaries at Mossack-Fonseca, the law firm at the heart of the Panama Papers, where they essentially sold Canada with the idea of bringing dirty money to Canada and it will be cleaned like the pure white snow; hence, ’snow-washing’ entered the Canadian anti-money laundering (AML) lexicon. This reputation came about because Canada has a weak corporate transparency regime, with exceptionally little beneficial ownership reporting and AML enforcement. Canada’s beneficial ownership gaps have been known and reported by international organisations for some time. There have been a number of AML updates in Canadian regulations in preparation of the 2021 Financial Action Task Force (FATF) peer review, but as of writing, the largest piece of the enforcement puzzle has been whether a publicly accessible beneficial ownership registry will be implemented. In this paper, we argue why a publicly accessible beneficial ownership registry is a critical tool that Canada can implement to end snow-washing. To make the case for a publicly accessible beneficial ownership registry, we will discuss the background of Canada’s beneficial ownership regime; global trends and findings on beneficial ownership registries and how Canada can navigate the challenges of implementing such a registry in a federated country as well as balance privacy concerns.
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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 | 0.020 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.033 | 0.017 |
| Scholarly communication | 0.022 | 0.008 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".