To Charge or Not to Charge: Prosecutorial Characteristics and Responses to Auditor Reported Findings
Bibliographic record
Abstract
ABSTRACT Deterring white-collar crime involves a complex relationship between detection by auditors and legal enforcement. We hand collect data from the Arkansas Legislative Audit Committee on auditor findings reported to the prosecuting attorney’s office, and subsequent prosecutorial responses for the respective district between 2015 and 2019. Applying theory from sociology and psychology we consider the effects of prosecutorial gender and financial resources (a dimension of socio-economic status) on prosecutorial charging decisions involving auditor reported fraud. We find an association between a prosecutor’s financial resources and formal prosecutorial charges but not for gender. We also find an increasing trend of auditor referrals but static levels of formal criminal charges filed by prosecutors. Our findings highlight the complexity of the auditor-prosecutor relationship, and a need to better understand differences between auditor and prosecutor conclusions regarding white-collar crime. Data Availability: Data are available from public sources cited in text. JEL Classifications: M420.
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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 | 0.004 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".