Determinants of and future violations following deferred prosecution and non‐prosecution agreements in corporate criminal cases
Bibliographic record
Abstract
Abstract The US Department of Justice's increasing use of deferred prosecution and non‐prosecution agreements (D/NPAs) over the past two decades has sparked debate about their merits compared with traditional plea deals, which often result in criminal convictions. This study examines the factors influencing prosecutors' decisions to use D/NPAs in disciplining firms for corporate misconduct. We analyze whether the likelihood of a firm's future misconduct varies by the type of discipline imposed, comparing the effectiveness of D/NPAs to traditional prosecution. Our findings reveal that prosecutors are more likely to employ D/NPAs with firms when a criminal conviction could cause significant economic harm to stakeholders. However, firms subject to D/NPAs are more likely to commit subsequent violations compared with those entering plea deals. As D/NPAs gain traction in the United States and internationally, our research highlights a trade‐off: while D/NPAs mitigate harm to innocent stakeholders, they are less effective at deterring future misconduct than traditional prosecutions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".