Electoral Competition, Moderating Institutions and Political Extremism
Bibliographic record
Abstract
Spatial models of electoral competition typically generate equilibria characterized by policy convergence and the median voter theorem. This paper assumes policy motivated parties who select candidates from various possible “types”, and introduces two further elements: stochastic voting outcomes and Constitutional provision of some bargaining power to the Opposition in policy choice. As in Wittman [1983], Calvert [1985] and Roemer [1997], policy divergence results. It is shown that moderating institutions can have a perverse effect on policy—political systems that allow greater bargaining strength to the Opposition usually generate more extreme policies. Indeed, for an intermediate range of the Opposition’s bargaining strength, we may have policy extremism, i.e, parties may implement policies more extreme than their own ideal points. Increased noise in voting and more extreme party tastes tend to generate more extreme policies. We also analyze a general model with fixed probabilities and find that policy extremism is almost always the case in this model. We discuss other applications involving delegated bargaining: oligarchies, secessionist movements and wage negotiations. Finally, it is shown that in an appropriately defined sense, elections have a moderating influence on policies, though post-election moderating institutions may not.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".