Bibliographic record
Abstract
How does governing in coalitions affect coalition parties’ responsiveness to voters? In this article, we seek to understand the relationship between political parties’ participation in multiparty governments and their responsiveness to voters. We argue that the extent to which coalition parties respond to policy priorities of voters is influenced by the divisiveness of policy issues within the cabinet and the ministerial responsibility for policies. To test our hypotheses, we combine data on the issue attention of 55 coalition parties from the Comparative Manifestos Project with data on government composition and data on the policy priorities of voters from the Comparative Study of Electoral Systems and various election studies in 45 elections across 16 European countries from 1972 to 2011. While we find that intra-cabinet divisiveness decreases coalition parties’ responsiveness, we find no effect for portfolio responsibility. Our findings shed light on the relationship between party competition and coalition governments and its implications for political representation.
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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 | 0.039 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".