Public Servants "Serving" Themselves: Occupational Fraud In Government
Bibliographic record
Abstract
From 1990 to 2012, Rita Crundwell committed the largest municipal embezzlement in U.S. history, stealing more than $53 million from the city of Dixon, Illinois. As city comptroller and treasurer, she secretly opened a bank account in the name of Dixon that only she controlled. Crundwell transferred money from city bank accounts into her illegitimate account, concealing the movement through fictitious invoices she submitted to the city. She used the money to finance a lavish lifestyle that included multiple residences, numerous vehicles, jewelry, and multiple horse-farming operations for her championship show horses. After another city employee accidentally discovered the secret account, Crundwell admitted her guilt and received a 20-year sentence. Crundwell’s fraud was alarming, audacious, and attention-getting — enough to be the subject of television episodes (such as CNBC’s American Greed and the Canadian Broadcasting Corporation’s The Fifth Estate) and a documentary movie (All the Queen’s Horses). However, it was not an isolated event. Occupational fraud in government is such a common occurrence that it should be a major concern to all government organizations and constituents.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| 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".