A Man’s world? Comparing the structural positions of men and women in an organized criminal network
Bibliographic record
Abstract
Abstract The crime gender gap is the difference between the levels of participation of men and women in crime, with men responsible for more crime than women. Recent evidence suggests that the crime gender gap is closing, both in crime in general and in organized crime. However, organized crime differs from other forms of criminal activity in that it entails an organizational structure of cooperation among offenders. Assessing whether the gender gap in organized crime is narrowing is not only about the overall levels of involvement of women, but about their roles and positions within the organized criminal structure, because the involvement of women does not mean that they are in influential positions, or that they have power or access to resources important for the commission of organized crime. This paper uses a social network approach to systematically compare the structural positions of men and women in an organized criminal network. We use a dataset collected by Canadian Law Enforcement consisting of 1390 individuals known or suspected to be involved in organized crime, 185 of whom are women. Our analysis provides evidence for an ongoing gender gap in organized crime, with women occupying structural positions that are generally associated with a lack of power. Overall, women are less present in the network, tend to collaborate with other women rather than with men, and are more often in the disadvantageous position of being connected by male intermediaries. Implications for theory and law enforcement practice are discussed.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".