Bibliographic record
Abstract
Purpose The objective of this paper is to challenge some of the rhetoric pertaining to the “harm” caused by “dirty” money infiltrating into the “legitimate economy.” The arguments regarding the impact of dirty money have been used to justify enhancements to law enforcement powers, and increasingly invasive investigative strategies and intelligence gathering regimes. Design/methodology/approach The paper reviews the literature pertaining to the intersection between “dirty money” and “legitimate business” and looks at how some of the most notorious criminal operations have been handled by the press and the courts. The paper examines corporate complicity in situations involving premeditated, ongoing criminal conduct and discusses two specific ways in which societies acknowledge and accommodate criminality within the operation of these corporations. Findings The paper argues that one must never minimize the amount of legitimate business that involves dirty money or uses dirty opportunities or was once dirty and is now legitimate or was legitimate and is now dirty. Practical implications The pretense of a clear separation between criminality and corporate operations is “useful” and is occasionally correct – but not as the norm and ought not to be the operating law enforcement expectation. Originality/value The paper encourages the reader to question the easily repeated claims about the financial threats from stereotypical forms of “organized crime,” while either dismissing or re‐defining the equally serious, or more serious, activities of professions (lawyers, accountants, bankers, politicians, government officials, corporate CEOs, etc.) operating supposedly legitimately.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.050 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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".