Quantitative Research on Global Terrorist Attacks and Terrorist Attack Classification
Bibliographic record
Abstract
Terrorist attacks are events which hinder the development of a region. Before the terrorist attacks, we need to conduct a graded evaluation of the terrorist attacks. After getting the level of terrorist attacks, we can fight terrorist organizations more effectively. This paper builds rating models for terrorist attacks, hidden or emerging terrorist organization classification discovery models, terrorist organization alliance network models and more, through quantitative research of the Global Terrorism Database, which solved the event classification. Through studying relevant literature and the variables of the Global Terrorism Database, this paper sorted out 25 observation variables related to the impact level (level of harm) of terrorist attacks. By establishing a mathematical model of factor analysis, 11 factors related to the impact level (level of harm) of terrorist attacks were constructed, and the variance of the contribution of each factor was used as the weight to calculate the comprehensive rate of the impact level of each terrorist attack. Finally, K-means clustering method is used to cluster and analyze the comprehensive rate of impact level, and the top 10 terrorist attacks with the highest impact level in the past two decades were obtained.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".