Quantitative Analysis of Factors of Terrorist Activities: A Systematic Review
Bibliographic record
Abstract
Since the beginning of the 21st century, the number of empirical studies devoted to the analysis of factors influencing the risks of terrorist activity has grown significantly. At the same time, assessments of the influence of individual factors may differ in various studies, due to which there is a need for a generalizing work that will consider the key results of the studies. The last generalizing works were published in English in 2011. Since then, a large number of works have appeared that clarify the influence of various determinants of terrorism. This study presents an analysis of the results of quantitative studies of factors influencing terrorist activity. As part of the study, 75 papers published in 2011–2022 have been analyzed. The most widely studied determinants of terrorism can be divided into three groups: political, social and economic. A total of 53 factors were identified, the statistical significance of which was demonstrated in at least two studies. Studies of the factors of terrorist destabilization of the last ten years have yielded the following main results. They have shown that countries with a hybrid political regime (anocracy), in a state of internal or external conflict, with a weak central government (for example, “fragile” or “failed” states), with an intermediate level of socio-economic development (i.e. with intermediate levels of GDP per capita, urbanization and education) have the greatest risks of terrorist destabilization, although in recent years the zone of greatest risk of terrorist activity has shifted somewhat towards the socio-economically least developed countries. In addition, these states are characterized by low rates of economic growth, high inflation, large amounts of foreign financial aid, high levels of inequality, a fairly large population, pronounced discrimination against minorities, as well as high levels of repression and terrorist activity in previous years.
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How this classification was reachedexpand
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.008 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".