Auditing for fraud and corruption: A public-interest-based definition and analysis
Bibliographic record
Abstract
To better understand how the practice of auditing can be more effectively enrolled in the fight against fraud and corruption, this study (1) examines how these problems are viewed and defined by the public and (2) contrasts this view and definition with that of professional auditors. The examination is informed by the dispositive theory of Foucault and an inductive analysis of a large (90,000+) multi-year sample of news stories related to fraud and corruption in the Italian health sector. While auditors define these problems in relatively narrow terms and consign them to ‘a form of risk, a threat to reputation and revenue, and a cost of doing business,’ the study finds that the public has a broader definition and a greater concern with problematic acts and actors ‘in and of themselves’. These findings have important implications for the audit expectations gap and how it might be addressed. The study also provides a useful analytical method for locating and better understanding fraud and corruption in other large, institutional settings.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".