Assessing the Risk Posed by Terrorist Groups: Identifying Motivating Factors and Threats
Bibliographic record
Abstract
While terrorist organizations have been analyzed for their motivations and tactics, little has been done to develop a systematic understanding of what makes some groups more dangerous than others. Knowing what makes some groups more threatening than others, or what conditions can influence a single group to become more or less of a threat, would help governments to prioritize resources during counterterrorism efforts. Using an approach similar to Ted Robert Gurr's assignment of a risk score to identify impending minority group rebellion, this article develops and tests a set of terrorist organizational characteristics. A two-phased approach is used. First, the authors identify key characteristics that could be anticipated to drive groups to be more active or deadly. The characteristics were identified and measured for terrorist groups for 1990–1994. The authors test group characteristics against subsequent group violence intensity from 1995 to 1999. Findings indicate that some group characteristics, such as religious ideology and group size, are important to understanding a group's relative level of violence. Though the study focused on a relatively short period of time, the findings indicate that a more comprehensive study of the impact that group characteristics have on violence levels would be a worthwhile undertaking.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".