Calling It Quits: Legislative Retirements in Comparative Perspective
Bibliographic record
Abstract
Although retirements are a major source of legislative turnover, research on the topic has been limited, especially outside of the US House of Representatives. In this article, we address this shortcoming by examining retirements in two countries with similar electoral systems yet different legislative environments and party systems: Canada and the United Kingdom. In particular, we extend analysis on the Congress that has consistently shown Republican members retire at higher rates than their Democratic counterparts to examine whether this finding is generalizable to legislators from other parties of the right and/or favouring devolution in other parliamentary settings. In presenting data that support many of these hypotheses, we explore an important normative implication: because their partisan predispositions make them less willing to serve, politicians from parties favouring limited government and/or devolution may be less able to translate their vision of politics into policy because they face systemic problems maintaining legislative seats.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".