Bibliographic record
Abstract
Purpose The purpose of this paper is to analyze FTX cryptocurrency frauds. FTX is a former cryptocurrency exchange platform that went bankrupt because of fraud in 2022. Design/methodology/approach Using a qualitative method and a case study of FTX, the authors document the multiple fraud schemes perpetrated. The authors collected media and research articles that discussed the FTX case. The authors analyzed 18 articles. Findings Based on this case, the authors highlight the governance and ethics weaknesses in the FTX environment. The authors also discuss cryptocurrency risks and regulation of cryptocurrencies. The FTX affair has shaken up the international regulatory world, which has been seeking solutions to protect customers and investors and helping banks take positions since 2022. Originality/value This study contributes to the fraud literature by deeply examining cryptocurrency fraud risks. In addition, the findings could help financial institutions and guide them in the cryptocurrency world.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".