The Electoral Consequences of Party Switching in Canada: 1945–2011
Bibliographic record
Abstract
Abstract This article addresses the overlooked subject of party switching in the Canadian House of Commons for the period 1945–2011. We estimate a model that explores how and why MPs engage in the otherwise risky behaviour of abandoning their party labels in a system characterized by a low personal vote. Our findings suggest that the electoral consequences for MPs who switch parties for policy reasons are indistinguishable from MPs who do not switch at all. By contrast, MPs who switch parties for office-related reasons, such as to accept a seat in cabinet or vote-related reasons, experience large electoral penalties. We also find that MPs who are expelled from caucus face the strongest electoral penalties of all party switchers, indicating it matters whether an MP jumps or is pushed. Our findings suggest that voters recognize opportunistic behaviour among their legislators and punish them accordingly and that under some circumstances, party switching may be both strategic and rational.
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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 | 0.002 | 0.002 |
| 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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".