The Impact of Party Organization on Electoral Outcomes
Bibliographic record
Abstract
Résumé Nous proposons un modèle de concurrence électorale dans lequel les partis fonctionnent comme des « marques » et peuvent exploiter la concurrence à l’intérieur du parti pour améliorer leur image. Nous démontrons que la concurrence entre les candidats des partis et les candidats indépendants permet d’expliquer la corrélation positive entre inégalités économiques et polarisation politique telle que documentée par McCarty, Poole et Rosenthal [2006] pour les États-Unis. Nous démontrons aussi que, lorsque les électeurs sont mal informés de la qualité des candidats, il est optimal pour le parti d’utiliser un système de primaires de façon à asseoir leur domination et vaincre les candidats indépendants. Ceci permet d’expliquer l’introduction du système des primaires directes aux États-Unis au début du xx e siècle. JEL Codes: D23, D72, D82.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".