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Record W3123088133 · doi:10.5539/ijef.v13n2p35

Disguised Terrorism Versus Political and Economic Failures- Which Diagnosis Do We Need to Recognize? 205 Countries in Two Decades of Analysis

2021· article· en· W3123088133 on OpenAlexvenueno aff
Amr Hany Saleh, Nader Alber

Bibliographic record

VenueInternational Journal of Economics and Finance · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsnot available
Fundersnot available
KeywordsTerrorismPoliticsPhenomenonFeelingSubject (documents)Political sciencePolitical violencePositive economicsPolitical economyArabicDevelopment economicsSociologyEpistemologyPsychologySocial psychologyLawEconomicsComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.005
Science and technology studies0.0020.012
Scholarly communication0.0100.011
Open science0.0010.003
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.022
GPT teacher head0.337
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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Same venueInternational Journal of Economics and Finance→Same topicTerrorism, Counterterrorism, and Political Violence→French-language works237,207→