Terrorist Attacks Escalate in Frequency and Fatalities Preceding Highly Lethal Attacks
Bibliographic record
Abstract
Highly lethal terrorist attacks, which we define as those killing 21 or more people, account for 50% of the total number of people killed in all terrorist attacks combined, yet comprise only 3.5% of terrorist attacks. Given the disproportionate influence of these incidents, uncovering systematic patterns in attacks that precede and anticipate these highly lethal attacks may be of value for understanding attacks that exact a heavy toll on life. Here we examined whether the activity of terrorist groups escalates--both in the number of people killed per attack and in the frequency of attacks--leading up to highly lethal attacks. Analyses of terrorist attacks drawn from a state-of-the-art international terrorism database (The Global Terrorism Database) showed evidence for both types of escalation leading up to highly lethal attacks, though complexities to the patterns emerged as well. These patterns of escalation do not emerge among terrorist groups that never commit a highly lethal attack.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".