Portfolio Salience and the Proportionality of Payoffs in Coalition Governments
Bibliographic record
Abstract
A fundamental divide has emerged over how portfolio payoffs are distributed among parties in parliamentary coalitions. On one side lies very strong empirical evidence that the parties in a governing coalition tend to receive portfolios in one-to-one proportion to the amount of legislative support they contribute to the coalition, with perhaps some slight deviations from proportionality coming at the expense of larger parties that lead coalition negotiations. On the other side of the debate lies a stream of formal theories that suggest the opposite – that parties in charge of coalition negotiations ought to be able to take a disproportionately large share of portfolio benefits for themselves. In this article, we address this disjuncture by re-examining the empirical connection between legislative seats and portfolio payoffs with the aid of a new and more extensive dataset, a different method of analysis, and what we see as a more valid operationalization of the dependent variable. This operationalization involves the inclusion, for the first time, of evidence concerning the importance or salience of the portfolios each party receives, as opposed to just their quantity. The article concludes with an assessment of the implications of our findings for the debate over the rewards of coalition membership in parliamentary democracies.
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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 | 0.011 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".