The Cross-National Determinants of Legislative Party Switching
Bibliographic record
Abstract
Why do legislators switch parties? What accounts for variation in party switching across different countries? How do electoral rules impact legislative party switching behavior and how is this behavior impacted by changes to these rules? The first chapter of this study builds on the existing body of research on the determinants of legislative party switching. More specifically, I build on the extant theories which have identified vote-, office-, and policy-seeking as motivations of legislator behavior. I examine the strategic decision making of legislators in various institutional contexts and argue that such contexts create or modify incentives and constraints that condition the decision to switch parties. Moving beyond the single country and cross-national party level analyses prevalent in the literature, this study attempts to approach party switching with a cross-national battery of variables from an original individual-level dataset. This dataset includes observations from Canada, Italy, New Zealand, and the United Kingdom from 1990-2001. I find support for vote- and policy-seeking hypotheses as well as district and system level institutional variables. In order to investigate how electoral rules impact party switching behavior, the second section of this study focuses on New Zealand and the evolution of its electoral system. These changes include a transition from a pure single-member district (SMD) electoral system to a mixed-member (MM), compensatory proportional representation system in 1996. Preliminary evidence suggests that the change to a MM electoral system is associated with a rise in the frequency of legislative party switching in New Zealand's House of Representatives. Additionally, there is evidence that party switching legislators are motivated by vote-seeking concerns over reelection.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".