The Primary Effect: Preference Votes and Political Promotions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this analysis of how electoral rules and outcomes shape the internal organization of political parties, we make an analogy to primary elections to argue that parties use preference-vote tallies to identify popular politicians and promote them to positions of power. We document this behavior among parties in Sweden's semi-open-list system and in Brazil's open-list system. To identify a causal impact of preference votes, we exploit a regression discontinuity design around the threshold of winning the most preference votes on a party list. In our main case, Sweden, these narrow “primary winners” are at least 50% more likely to become local party leaders than their runners-up. Across individual politicians, the primary effect is present only for politicians who hold the first few positions on the list and when the preference-vote winner and runner-up have similar competence levels. Across party groups, the primary effect is the strongest in unthreatened governing parties.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| 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 it