Bibliographic record
Abstract
Social researchers are facing more and more challenges as criminal networks are expanding in size and moving to the Internet. Many efforts are currently under way to enhance the technical capabilities of researchers working in the field of cybercrimes. Rather than focusing on the technical tools that could enhance research performance, this article focuses on a specific field that has demonstrated its use in the study of criminal networks: social network analysis (SNA). This article evaluates the effectiveness of SNA to enhance the value of information on cybercriminals. This includes both the identification of possible targets for follow-up research as well as the removal of subjects who may be wasting the researchers’ time. This article shows that SNA can be useful on two levels. First, SNA provides scientific and objective measures of the structure of networks as well as the position of their key players. Second, fragmentation metrics, which measure the impact of the removal of n nodes in a network, help to determine the amount of resources needed to deal with specific organisations. In this case study, a tactical strike against the network could have had the same destabilising impact as a broader approach. The resources saved by limiting the investigation targets could then be used to monitor the criminal network’s reaction to the arrests and to limit its ability to adapt to the post-arrest environment.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".