Reaching across the aisle: Explaining government-opposition voting in parliament
Bibliographic record
Abstract
The divide between government and opposition is clearly visible in the way members of parliament vote, but the variation in government–opposition voting has been left relatively unexplored. This is particularly the case for contextual variation in the extent to which parliamentary voting behaviour follows the government–opposition divide. This article attempts to explain levels of government–opposition voting by looking at three factors: first, the majority status of cabinets (differentiating between majority and minority cabinets), cabinet ideology (differentiating between more centrist and more extremist cabinets) and norms about cabinet formation (differentiating between wholesale and partial alternation in government). The study includes variation at the level of the country, the government and the vote. The article examines voting in the Netherlands (with a history of partial alternation) and Sweden (with a history of wholesale alternation). We find strong support for the effect of cabinet majority status, cabinet ideology and norms about cabinet formation on government–opposition voting.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".