The ties that bribe: Corruption's embeddedness in Chicago organized crime*
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
Abstract The crime of corruption ranges from politicians involved in high‐profile scandals to low‐level bureaucrats granting contracts and police officers demanding bribes. Corruption occurs when state actors criminally leverage their positions of power for financial gain. Our study examines how corruption varies by political power position and within criminal contexts by measuring the embeddedness of corruption within Chicago historical organized crime. We analyze Chicago's organized crime network before and during Prohibition (1900–1919 and 1920–1933) to compare differences across embedded network positions between political, law enforcement, and nonstate actors. Our findings show that more police were in organized crime than politicians before Prohibition, but the small group of politicians had higher embeddedness in organized crime. During Prohibition, when organized crime grew and centralized, law enforcement decreased in proportion and became less embedded in organized crime. Politicians, however, maintained their proportion and high level of embeddedness. We argue that everyday corruption is more frequent but less embedded when criminal contexts are moderately profitable. As criminal contexts increase in profitability, however, corruption moves up the political ladder to include fewer people who are more highly embedded. This work has theoretical implications for the symbiotic relationship between corruption and criminal organizations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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