Terrorist Attacks Against Firefighters, 1970-2019
Bibliographic record
Abstract
Firefighters are a critical component of the emergency response system and therefore a potential target for organizations seeking to disrupt this system. Terrorist organizations may deliberately attack firefighters to both increase the devastation of an attack and impair the affected community's ability to respond to an attack. We performed a focused search of the Global Terrorism Database to identify terrorist attacks against firefighters worldwide. The database includes incidents from 1970 through 2019, with a total of 201,183 entries. These entries were searched for incidents involving firefighters or fire trucks. We analyzed trends in the number of incidents occurring per year, regions of the world impacted, methods employed, and number of casualties inflicted. A total of 42 attacks involving firefighters were identified in the Global Terrorism Database resulting in 26 deaths and 95 wounded. Of the 42 attacks, 12 (28.6%) were secondary attacks, where firefighters responding to an initial attack were themselves targeted. The most common method for both primary and secondary attacks was the use of a bomb or explosive. Although attacks against firefighters are uncommon, they highlight both the strategic value and vulnerability of firefighters to terrorist attacks. Increased efforts must be made to protect firefighters from future terrorist attacks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".