Closest to the People? Incumbency Advantage and the Personal Vote in Non-Partisan Elections
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
Do incumbents dominate non-partisan elections because of an especially large personal vote? This question has important implications for understanding the causes of incumbent success and the benefits or drawbacks of non-partisan elections. This paper uses a natural experiment, combined with three original datasets, to estimate the size, persistence, and consequences of the personal vote in a large non-partisan city election. We first use individual-level survey data to show that individuals assigned quasi-randomly to a new incumbent are substantially less likely to support the incumbent. We use a second survey, one year later, to demonstrate the persistence of this effect. Finally, we use historical election results to simulate the electoral consequences of the personal vote; we find that the personal vote is sufficiently large to affect one in four incumbent races. We conclude that the personal vote, while large and important, is not sufficient to explain incumbent dominance in non-partisan contests.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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