Disguised Terrorism Versus Political and Economic Failures- Which Diagnosis Do We Need to Recognize? 205 Countries in Two Decades of Analysis
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,006 | 0,005 |
| Études des sciences et des technologies | 0,002 | 0,012 |
| Communication savante | 0,010 | 0,011 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».