Do parties converge to the electoral mean in all political systems?
Bibliographic record
Abstract
Many formal models suggest that parties or candidates should locate at the electoral mean. Yet, there is no consistent evidence of such convergence across political systems. Schofield’s (2007) Valence Theorem proves that when valence differences across parties are large, there is non-convergence to the mean. Convergence to the mean depends on the value of the convergence coefficient, c. When c is high there is significant centrifugal tendency acting on the parties and when c is low there is a significant centripetal tendency acting on the parties. In this paper we apply the stochastic valence model of elections in various countries under different political regimes and use the convergence coefficient of these elections to classify political systems. Our results show that the convergence coefficient varies across elections in a country, across countries using the same political system and across political regimes. For countries using proportional representation, namely Israel, Turkey and Poland, the centrifugal tendency is very high and parties move away from the mean. In the majoritarian polities of the United States and the UK, parties are located at the mean, as the centrifugal tendency is very low. In anocracies, the autocrat imposes limitations on how far from the origin the opposition parties can move but the equilibrium is fragile.
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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".