Priors rule: When do Malfeasance Revelations Help or Hurt Incumbent Parties?
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
Effective policy-making requires that voters avoid electing malfeasant politicians. However, as our simple learning model emphasizing voters' prior beliefs and updating highlights, informing voters of incumbent malfeasance may not entail sanctioning. Specifically, electoral punishment of incumbents revealed to be malfeasant is rare where voters already believed them to be malfeasant, while information's effect on turnout is non-linear in the magnitude of revealed malfeasance. These Bayesian predictions are supported by a field experiment informing Mexican voters about malfeasant mayoral spending before municipal elections. Given voters' low expectations and initial uncertainty, as well as politician responses, relatively severe malfeasance revelations increased incumbent vote share on average. Consistent with voter learning, rewards were lower among voters with lower malfeasance priors, among voters with more precise prior beliefs, when audits revealed greater malfeasance, and among voters updating less favorably. Furthermore, both low and high malfeasance revelations increased turnout, while less surprising information reduced turnout.
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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 | 0.013 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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