How Corruption Hits People When They Are Down
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
Using cross-country and Peruvian data, I show that victims of misfortune, particularly crime victims, are much more likely than non-victims to bribe public officials. Misfortune increases victims' demand for public services, raising bribery indirectly, and also increases victims' propensity to bribe certain officials conditional on using them, possibly because victims are desperate, vulnerable, or demanding services particularly prone to corruption. The effect is strongest for bribery of the police, where the increase in bribery comes principally through increased use of the police. For the judiciary the effect is also strong, and for some misfortunes is composed equally of an increase in use and an increase in bribery conditional on use. The expense and disutility of bribing thus compound the misery brought by misfortune.
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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 | 0.009 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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