Canada's Integrity Regime: The Corporate Grim Reaper
Bibliographic record
Abstract
In 2019, SNC-Lavalin made global headlines after it was revealed that the Canadian Prime Minister, Justin Trudeau, had interfered in the prosecution of the company for the bribery of Libyan officials. Although the scandal was primarily viewed as political, it also highlighted flaws in Canada’s Integrity Regime; specifically, the regime’s unworkable and draconian approach to debarment. This Article will address the pressing need in Canada to modify its debarment remedy and enact a system that more effectively protects the government’s interests. To illuminate the current issues facing Canada’s Integrity Regime, this Article will begin by examining Canada’s debarment system, outlining the various iterations of the Integrity Regime. The Article then examines the debarment policies of a more mature and flexible debarment regime, focusing on Canada’s neighbor and trading partner—the United States. It considers the history of this regime and outlines the scope of debarment officials’ roles in this more forward-looking system. The Article next considers the repercussions of Canada’s current approach to debarment, using the SNC-Lavalin affair as a case study. The Article concludes by recommending that Canada implement a discretionary debarment regime allowing government officials to make decisions that are in the best interest of the Canadian government and the population that it governs.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.044 | 0.020 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".