Economic development, education, and terrorism: A quantitative analysis
Bibliographic record
Abstract
Quantitative cross-national tests using negative binomial regression confrmthe existence of a curvilinear relationship between the amount of people receiving an education and the level of terrorist activity in certain countries. In countries with the lowest level of educational modernization, the growth of education is accompanied by a signifcant trend towards an increase in the intensity of terrorist activities, and this trend turns out to be signifcant after being controlled for economic development level, type of political regime, unemployment, economic inequality and urbanization. At the same time, a pronounced extreme has been detected given a relatively low but not completely absent quantitative development level of national education systems (corresponding to 3–6 years of schooling on average). In more socio-economically developed countries, a further increase in the years people on average spend receiving education is accompanied by a signifcant trend towards a decrease in the level of terrorist activity. This trend alsoturns out to be signifcant when controlling for economic development level, type of political regime, unemployment, economic inequality and urbanization. The sharpest decline corresponds to the range of 7–8 years spent on average receiving education. On the one hand, the conducted quantitative analysis allows us to make an optimistic conclusion, in that a further increase in the years people on average spend receiving education – together with further economic development of the middle and high income countries – can indeed become one of the factors which will lead to a decrease in the level of terrorist activity in these countries. The analysis also shows that, for further reduction of the level of terrorist activity (in addition to the growth of the level of education), a decrease in the level of unemployment, economic inequality, the spread of consolidated democratic political regimes and the reduction of the amount of factional conflict partial democracies can also play a signifcant role. At the same time, the growth of economic inequality and the level of unemployment, the rejection of change in the world by traditionalist members of the population – all of this may become the cause foran increase in the level of terrorist violence in frst world countries.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".