Bibliographic record
Abstract
Corporate fraud is often presumed to be the type of crime that can be deterred. Those who embrace deterrence as a goal of law enforcement, however, often ignore the tradeoffs between the deterrence of potential offenders and the deterrence of those "mid-fraud perpetrators" who are already mid-way through illicit schemes when the government announces a change in policy. Unlike potential offenders, mid-fraud perpetrators have no incentive to cease criminal conduct in response to increases in sanctions or likelihood of detection. This is true because a "link" exists between the offenders' cessation of future misconduct and the probability that their prior conduct will be detected and punished. If a CFO has lied to a company's shareholders in Quarter 1 about the company's profits, his cessation of lying in Quarter 2 substantially increases the chances that someone will focus on and detect his previous lies in Quarter 1. The problem with this linkage between cessation of conduct and increased probability of punishment is that criminal sanctions aimed primarily at deterring new offenders may also encourage perverse reactions from perpetrators in the midst of frauds. Policymakers contemplating changes in law enforcement policy therefore should consider the linkage problem in calculating the benefits and drawbacks of different law enforcement strategies.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".