Disguised Terrorism Versus Political and Economic Failures- Which Diagnosis Do We Need to Recognize? 205 Countries in Two Decades of Analysis
Bibliographic record
Abstract
Identifying the causes of terrorism has been a goal of researchers for decades. The evidences and implications of terrorism are both extremely ambiguous, but also poignant. Dealing with terrorism has become the centerpiece of political debates for years. Despite of that, it has always been followed by the similar and identical uncompromising and intransigent security measures in different parts of the world, even if the reasons behind the acts combine many and different types of human sides, including political, social, security, psychological, cultural, and religious dimensions. There are lots of tremendous feelings, not only for the victims but also for the assailants that believe in their unprejudiced acts and are continuously able to justify their significance of the use of violence. That is why the paper started by introducing the subject to the reader, including the terms related to the phenomena, but also introducing the idea that there is an economic cost associated with this phenomenon. A key challenge of understanding terrorism is both defining the various and multidimensional theoretical and practical features of extremism, while, at the same time trying to render the various Political and Economic impacts of terrorism on societies. With effort to help the different spheres to understand the roots of this phenomenon, we thought that it was necessary to bring the widest and assorted point of views related to the roots, and also failures that might lead to violence, in particular from political and the economic perspectives, from different countries. We have also added English, French, Spanish and Arabic references written in their native languages. Empirically, we have chosen to assess the Political and the Economic drivers for Terrorism. Political drivers have been measured by “Control of Corruption” (X4), “Government Effectiveness” (X5), “Regulatory Quality” (X6), “Rule of Law” (X7) and “Voice and Accountability” (X8). Economic determinants are used as control variables in the robustness check and they have been measured by “GDP growth” (X1), “GDP per capita” (X2) and “Employment Ratio” (X3). Using panel data analysis according to GMM technique results indicate that all of these political drivers have significant positive effects on “Political Stability”. Analysis has been conducted using annual data of 205 countries during the period from 2002 to 2019. Robustness checks indicates that controlling for economic factors has slightly enhanced the explanation power, providing R2 of 0.2498 instead of 0.1836 (for the first hypothesis), of 0.8928 instead of 0.8853 (for the second hypothesis), of 0.2333 instead of 0.1748 (for the third hypothesis), of 0.8941 instead of 0.8869 (for the fourth hypothesis) and of 0.9920 instead of 0.9821 (for the fifth hypothesis). The paper concludes that terrorism is mainly caused by political drivers. Economic factors had a slight impact and enhanced very much the explanation power of the model. Nevertheless, mixing political and economic considerations have shown that that terrorism is predominantly due to lack of political lacunas, and not for the most part to economic needs.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".