Leaders and Leadership in Criminal Activities: A Scoping Review
Bibliographic record
Abstract
The objective of this article is to conduct a scoping review of existing literature on the subject of leadership within the criminal domain. Drawing from theoretical models used to explain leadership in social organizations, this study analyzed 71 articles presenting findings or reflections contributing to a better understanding of leaders and leadership within criminal organizations. The results yielded three overarching themes and eight sub-themes for analysis. The first theme focuses on individual factors associated with leaders and leadership in criminal contexts, and it has been subdivided into three sub-themes: Behavioral factors of criminal leadership, psychosocial factors of criminal leadership, and Gender and leadership in crime. The second theme concentrates on managerial approaches within criminal organizations and is further divided into four sub-themes: Hierarchy, Leadership and criminal organization, selection of leaders in criminal organizations, distribution of leadership in criminal organizations, and the role of leaders in criminal organizations. Finally, the last identified theme examines the phenomenon of leadership decapitation, which is elaborated upon in two sub-themes: the leadership decapitation effect and leader protection and prevention of the decapitation effect. The results are discussed in the context of existing general knowledge on leadership and serve to develop theoretical implications for future studies.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".