Bibliographic record
Abstract
Typologies are intended to assist researchers in understanding complex social phenomena. This paper reviews the current literature on organised crime typologies and argues that the majority of organised crime typologies are reflected to some extent in a typology developed by the United Nations Office on Drugs and Crime (UNODC) in 2002. Organised crime typologies can be categorised into three groups: models that focus on the physical structure and operation of an OCG, the activities of OCGs and the social, cultural and historical conditions that facilitate organised crime activity. This paper will only discuss models that examine the physical structure and operation of an OCG; the UNODC typology is exclusively focused on structural elements. Typologies on organised crime structure have developed largely in isolation from each other and appear disparate. This paper will analyse the formation of each typology to establish their individual elements. It will then identify which typologies and their respective characteristics can be aligned with or distinguished from the UN typology. The value of this review is that it will enable greater uniformity and consistency in academic discussion on organised crime typologies.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".