Bibliographic record
Abstract
Purpose The initial role of computers and information technology (IT) systems consisted of improving business daily's operation. However, this quest of efficiency serves more obscure goals as fraudsters exploit the electronic dimension for personal profits with a maximum devastating impact on businesses and their client. The purpose of this paper is to suggest an analysis of the role of the electronic dimension in financial market crimes. It proposes reconsidering its importance based on its role rather than on its complexity and, consequently, better understanding the basic elements of a fraud. Design/methodology/approach The paper takes the form of a case analysis and field work. Findings The complexity of an IT system facilitates the commitment of a fraud and, at the same time, complicates its investigation. However, an IT system does not initiate a fraud. It is an accessory, a tool at the service of a criminal mind which is where the scheme originates. Research limitations/implications Information regarding the two case studies comes only from public sources (mainly written media and books) and is not confirmed by any confidential data available to the author. Originality/value Many computer crime experts (re: Cybercriminality Conference in Canada, April 2008, and in Luxembourg, June 2008) agree upon accessorial aspect of IT systems. For investigation purposes, it switches the focus from the computer element back to the main event: the environment in which the fraud occurs.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".