Two Decades of Accounting Fraud Research: The Missing Meso-Level Analysis
Bibliographic record
Abstract
Drawing on an analysis of 208 articles, this paper argues that while the fraud literature varies on the diagnosis of fraud, it is rooted in a common narrative as to its nature and causes. Specifically, this paper adopts an investigative approach to understand how fraud is often researched and shapes audit policies and practices. The findings reveal that fraud is generally looked at as an individual and/or organizational phenomenon, thereby allowing the meso-level of analysis to escape scrutiny. A gap, therefore, exists in being able to detect fraud at the meso-level, such as in financial services (i.e., LIBOR rigging). A meso-level analysis of fraud will allow researchers to highlight problems across the general field like the banks rigging the LIBOR or distorting the currency market as in the forex scandal. A meso-level analysis of fraud is important because it highlights the contagious behavior across the field and offers insights for fraud prevention and detection. Recognizing this unique epistemology will allow researchers to uncover new knowledge and not remain wedded to a reified understanding of fraud and fraud risks. Policymakers can derive insights and draft policies that reflect practitioners’ needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.026 | 0.026 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".