Corruption and Turnout in Presidential Elections: A Macro‐Level Quantitative Analysis
Why this work is in the frame
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Bibliographic record
Abstract
This article tests the impact of corruption on electoral turnout for over 200 elections from over 70 presidential systems conducted between 1990 and 2011. Differentiating among three corruption indicators (i.e., the I nternational C ountry R isk G uide corruption indicator, the T ransparency I nternational C orruption P erceptions I ndex, and the W orld B ank C ontrol of C orruption measure), I evaluate corruption's precise impact on electoral participation in a pooled time series framework. Controlling for compulsory voting, semi‐presidentialism, regime type, development, political culture, the closeness of the election, and state size, my results are nuanced. I find that corruption more narrowly defined as political corruption stifles turnout, whereas a rather broad definition of corruption, which includes societal and financial corruption, has no impact on macro‐level turnout. Finally, I discover that the interactive impact of corruption on other variables in the turnout function is rather limited. Related Articles Kostadinova , Tatiana 2009 . “.” Politics & Policy 37 (): 691 ‐ 714 . http://onlinelibrary.wiley.com/doi/10.1111/j.1747‐1346.2009.00194.x/abstract Caillier , James . 2010 . “.” Politics & Policy 38 (): 1015 ‐ 1035 . http://onlinelibrary.wiley.com/doi/10.1111/j.1747‐1346.2010.00267.x/abstract Lagunes , Paul F. 2012 . “.” Politics & Policy 40 (): 802 ‐ 826 . http://onlinelibrary.wiley.com/doi/10.1111/j.1747‐1346.2012.00384.x/abstract
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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