Party Organization and Electoral Competition
Bibliographic record
Abstract
We propose a model in which two parties select the internal organization that helps them win the election. Party choices provide incentives to the politicians who represent them. Depending on whether politicians are opportunistic or partisan, we identify four effects. First, a selection effect: intraparty competition gives parties more candidates to choose from. Second, an incentive effect: intraparty competition adds a hurdle and impacts on candidates' incentives. Third, a trust effect: because of the incentive effect, intraparty competition is a signal to uninformed voters. Finally, with partisan preferences, an ideology effect appears. Ideology is a public good in a competitive party and induces free riding. Intraparty competition is valuable when voters are badly informed or intraparty competition is weak. These results rationalize the introduction of direct primaries in the United States, the organizational changes in Western European parties since 1960, and the organizational differences between centrist and extreme parties. (JEL D23, D72, D81)
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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 | 0.000 | 0.000 |
| 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".