Terrorist Attacks Against Firefighters, 1970-2019
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.010 |
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 it