The portfolio allocation paradox: An investigation into the nature of a very strong but puzzling relationship
Bibliographic record
Abstract
Abstract Perhaps the strongest empirical finding in political science is ‘Gamson's Law’: the near‐perfect relationship that exists in parliamentary systems between a coalition party's seat contribution to the government and its quantitative allocation of cabinet portfolios. Nevertheless, doubts remain. What would happen if the salience or importance of the various portfolios was also taken into account? Should it not be the case that payoffs correspond with bargaining power rather than seat contributions? And perhaps most significantly, would addressing these issues produce evidence that the parties designated to form governments extract disproportionately large payoffs for themselves, as predicted by ‘proposer’ models of bargaining? Utilizing the results of a new expert survey of portfolio salience in 14 Western European countries, the authors of this article explore each of these questions. Their basic finding is that salience‐weighted portfolios payoffs overwhelmingly mirror seat contributions, contra proposer models and any other models based on bargaining power. The article concludes with a discussion of the implications for formal models of bargaining.
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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 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".