Terrorist Attacks to Essential Services, Infrastructures and Facilities in G7 Countries During the Period 2000-2020
Bibliographic record
Abstract
Starting from the terrorist events recorded in the Global Terrorism Database (GTD), a very detailed and original analysis has been performed on the evolution, starting from the attach to the Twin Towers in New York in 2001, over the last 21 years of terrorist attacks on specific targets related to critical infrastructures, essential services and facilities.Specifically, a set of targets extracted from the GTD referred to in the paper as ESIF (Essential Services, Infrastructures and Facilities) macro-target has been selected to carry out an original focus on terrorist events perpetrated in G7 countries (USA, UK, France, Germany, Italy, Canada and Japan).This ESIF macro-target typically contains most of a country's strategic industrial assets, infrastructure and services.The hereby analysis has been conducted in a timely manner for the period 2000-2020, in order to carry out a comparison of the different situations recorded in the most developed world countries, to intercept possible trends, also verifying the type of weapon used for the attacks, then focusing the analysis on CBREI (Chemical, Biological, Radiological, Explosive and Incendiary) attacks, which constitute the most destructive and impactful terrorist attacks found in the GTD.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".