The Interconnections between Tax Crime, Organized Crime, and Corruption
Bibliographic record
Abstract
Abstract This chapter explores the interconnections between tax crime, organized crime, and corruption from phenomenological, conceptual, and legal perspectives to shed light on under-explored phenomena and their impact on the EU institutions and Member States. On the one side, organized criminal groups often engage in sophisticated tax fraud schemes and exploit current limits in the legal and institutional frameworks at the national and EU levels to make illegal gains. On the other side, public officials in tax and customs administrations and law enforcement agencies involved in the fight against financial crimes can be enablers of tax evasion in cases of bribery and collusion with criminals. The dirty money generated by these criminal offences is also often cleaned through money laundering schemes developed with the assistance of some dishonest professionals. Organized tax crime and fiscal corruption, coupled with money laundering, are serious threats to security at the EU and national levels. Considering their correlation, they should be targeted explicitly from an operational perspective by enhancing an integrated model which combines a ‘whole-of-government approach’ and ‘responsibilization strategy’ in the public and private sectors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".