Bibliographic record
Abstract
This case focuses on fraud investigation in a not-for-profit organization, along with an examination of governance and management control practices. The student assumes the role of an accountant investigating a possible fraud. The student is first presented with sample invoices paid by the organization that are fraught with irregularities and red flags of potential fraud. Drawing on the student's knowledge of control systems and corporate governance, the student's task is to identify suspicions of possible fraudulent transactions, identify key suspects, and develop an investigative plan. The class can also discuss recommendations to improve governance and control mechanisms to avoid future occurrences of fraud. The case is presented in three parts, and closely parallels a fraud investigation as additional information is revealed in each successive part of the case. This is much like peeling the layers of an onion which is a common way to describe the evolution of a fraud investigation. This case is based on a real fraud investigation conducted by one of the authors who was engaged by the province's Ministry of Health. Students who express disbelief about issues portrayed in the case can be reassured that these faithfully represent actual events.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".