The SNC-Lavalin Affair: Justin Trudeau, Ministerial Resignations and Party Discipline
Bibliographic record
Abstract
The SNC-Lavalin affair ranks among the most spectacular cases of ministerial resignations and party discipline in Canadian history. In 2019, discord plunged Justin Trudeau’s Liberal government into turmoil amid explosive news stories, cabinet shuffles, committee testimony, an ethics investigation and party discipline. SNC-Lavalin, a Quebec-based company, faced criminal charges and urged the Trudeau government to negotiate an alternative, or else jobs might be lost. The Prime Minister’s Office, the clerk of the Privy Council and the minister of finance were at loggerheads with Attorney General Jody Wilson-Raybould over her refusal to do so. This chronicle situates the political events among antecedent cabinet crises of the Manitoba schools question in 1896, the conscription crisis in 1944 and nuclear armament in 1963. The 2019 episode was the first involving women ministers and social media, and the only one to erupt over a relatively obscure policy issue. All cases were a prelude to the governing party losing seats in the next election.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.074 | 0.034 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".