From Syndicates to Protocols: Rethinking Organized Crime in the Age of Cybercrime
Bibliographic record
Abstract
This article develops the concept of cyber-mediated organized crime to capture structural transformations driven by digital infrastructures, especially cryptocurrencies. Integrating functionalist theory (AGIL), Elias’s figurational sociology, and trust theory, it reconstructs how criminal formations adapt by substituting social embeddedness with cryptographic mechanisms. Empirical domains—ransomware, darknet markets, blockchain laundering—reveal how digital actors fulfill core functions of protection, coordination, and trust under pseudonymity and decentralization. Rather than replicating traditional hierarchies, these formations emerge as adaptive social systems shaped by functional differentiation and technological affordances. Their systemic resilience, despite evolving law enforcement strategies, underscores new modes of illicit governance and contestation.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.041 |
| Scholarly communication | 0.014 | 0.031 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".