Bribes and ballots: The impact of corruption on voter turnout in democracies
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
While officials involved in graft, bribery, extortion, nepotism, or patronage typically like keeping their deeds private, the fact that corruption can have serious effects in democracies is no secret. Numerous scholars have brought to light the impact of corruption on a range of economic and political outcomes. One outcome that has received less attention, however, is voter turnout. Do high levels of corruption push electorates to avoid the polls or to turn out in larger numbers? Though of great consequence to the corruption and voter-turnout literature, few scholars in either area have tackled this question and none has done so in a broad sample of democracies. This article engages in this endeavor through an analysis of the broadest possible sample of democratic states. Through instrumental variable regression we find that as corruption increases the percentage of voters who go to the polls decreases.
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
| 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