Bibliographic record
Abstract
Purpose – The purpose of this paper is to explore the role of the culpable insider and the predatory criminal in fraud and deception. Design/methodology/approach – Two groupings of fraud are considered in this paper. Insider fraud consists of a person within an organization misusing their position for corrupt self-dealing, asset misappropriation and financial statement fraud. Case studies are discussed, offering differing perspectives on the role of insiders. Fraudsters use technology, like malware, to take on the mantle of an insider to facilitate their larceny. This paper also looks at the role of the insider with predatory frauds. Findings – Most enterprises, be they public entities or private firms, are at risk of internal fraud. Internal financial controls are the first line of defence. In tougher economic times, when enterprises run on the tightest of margins, control mechanisms are at risk of being weakened at the altar of efficiency. Firms can also adopt cultures that deter frauds, either through policies on whistle-blowers or through simple employee screening procedures. For predatory frauds, the basic warning flag can be summed up with the cliché: if something seems too good to be true, it probably is. Originality/value – This paper synthesizes research on fraud and the role that an insider can play as well as the role of a predatory fraudster.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".