Tracking Transnational Terrorist Resourcing Nodes And Networks
Bibliographic record
Abstract
In light of persistent terrorist attacks in Europe and elsewhere, the study of terrorist resourcing and financing has attracted renewed attention. How are terrorists' networks financed? Who raises the financial "resources," and how do they transfer them across borders? How does the global financial industry facilitate or impede these transfers? Answers to these and other questions can help law enforcement investigate, disrupt, and neutralize cross-border terrorist resourcing. Evidence and data on this phenomenon is scarce, of questionable quality, irreplicable, and can be difficult to come by. This study is the first comprehensive effort to collect, code, analyze, and compare available open-source case law data on transnational terrorist resourcing networks. Under the study's methodology, the conventional yet strict focus on financing is broadened to resources, which includes forms other than cash, including trade-based fraud and online social networks. The analysis reveals common crossborder resourcing patterns and usage of financial intermediaries such as banks. It thus contributes to the ongoing optimization of anti-terrorist resourcing laws, policies, and risk-management practices.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".