An Analysis of Terrorist Attacks on Soft and Hard Targets in the Period 2000-2019
Bibliographic record
Abstract
With the aim of characterising the evolution of the phenomenon of terrorist attacks in the 20 years since 9/11, this paper conducts a broad analysis of terrorist events from 2000 to 2019, based on information made available by the Global Terrorism Database (GTD). The first part of the document illustrates the evolution of terrorist attacks worldwide, while the second part focuses on the type of targets favoured by terrorists. As a key result of the analysis, it will be shown that in recent years many attacks have been directed against simple public and private buildings, targeting and killing individuals, typically civilians. These types of targets have been referred to in the literature as soft targets, as opposed to the term hard targets or hardened structures, government, military, police and intelligence buildings and sites. In the work, specific definitions of soft target and hard target related to GTD information fields are proposed and evaluated over the period 2000-2019. Furthermore, detailed items of the terrorist targets, such as houses, schools, universities, restaurants, theatres etc., were considered and analysed. The evidence obtained provides an up-to-date view of terrorists' recent approaches to selecting targets and conducting attacks. The understanding of the evolution of these approaches can allow for better organisation of future prevention and protection of potential soft and hard targets.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.002 | 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".