Paying terrorist ransoms: Frayed consensus, uneven outcomes & undue harm
Bibliographic record
Abstract
Terrorist groups are believed to be financed, in part, by ransoms paid for kidnap victims. As part of global efforts to counter the financing of terrorism and prevent further terrorist attacks (including more kidnappings), the international community has attempted to implement a moratorium on the payment of ransoms. Despite a unified stance, ransom payments to terrorist groups have continued. An exploratory review of 20 countries reveals significant variation between public statements and private practice when it comes to ransom payments. While it is clear that states, organizations, and individuals are paying terrorist ransoms, it is far less clear what effect this has had on terrorism itself. A review of three case studies shows significant variation in the relationship (or perhaps, lack thereof) between ransom payments and terrorist attacks. These findings suggest a need for more study on the effects of ransom payments on terrorist capabilities, and a re-assessment of existing “no-ransom” policies.
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 | 0.059 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".