The Lingering Effect of Scandals in Congressional Elections: Incumbents, Challengers, and Voters
Bibliographic record
Abstract
Objective We have two goals. First, we investigate both the short‐ and long‐term electoral impact of involvement in scandals on reelection margins of incumbents in U.S. congressional elections. Second, we evaluate the impact of scandals on district‐level turnout. Methods We model the impact of involvement in a political scandal on incumbents’ electoral margins in the election cycle in which the scandal comes to light, as well as in future election cycles. We also model the impact of scandal on district‐level turnout. Results Involvement in a scandal exerts not only an immediate, negative effect on incumbents’ margins, but one that also lingers beyond the initial reelection cycle. Elections involving incumbents embroiled in scandals experience a small boost in turnout. Conclusion In tandem, these results implicate the mobilization of previous nonvoters intent on “throwing the bum out” as one mechanism through which incumbent vote share is depressed in scandal elections.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".