How Ideas and Strategic Learning Fostered the 2022 Agreement Between the Liberal Party of Canada and the New Democratic Party
Bibliographic record
Abstract
Abstract Why did the Liberal Party of Canada (LPC) and the New Democratic Party (NDP) enter into a supply-and-confidence agreement in March 2022? Interparty cooperation among federal parties is rare during minority governments, and yet the agreement created a formal alliance in the House of Commons. In this article, we argue that ideational factors led to the 2022 agreement. We examine the role of programmatic beliefs and strategic learning during the COVID-19 crisis and the 2019-2021 election sequence to shed light on changes in federal parliamentary strategies in Canada. From ad-hoc voting coalitions to extended cooperation on social policymaking, the LPC and the NDP learned how to work together in the House of Commons while using the agreement as a tool to compete with each other in anticipation of the next federal 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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.017 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".