Behind the SNC-Lavalin Scandal: The Transnational Diffusion of Corporate Diversion
Bibliographic record
Abstract
Abstract At issue in the SNC-Lavalin scandal was a new tool of corporate criminal law: remediation agreements. Introduced in 2018, remediation agreements allow corporate diversion and create an alternative to the prosecution of corporations suspected of criminal wrongdoing. This article examines why the federal government adopted and chose this particular new tool. Drawing on a wide-ranging documentary record, I argue that this reform was the product of transnational lawmaking and the ongoing influence of Canada's international commitments to prohibit and punish foreign bribery. The article shows how international criticism of Canada's lacklustre anti–foreign bribery enforcement record catalyzed cross-national policy diffusion and learning from other states. This led Canada to adopt corporate diversion, which promised greater enforcement, and also led Canada to adopt a form of the practice with legislative and judicial limits that narrowed the chances of any company—including SNC-Lavalin—of obtaining a remediation agreement.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".