The Cultural Roots of Contemporary Islamic Terrorism and Ways of Confronting It
Bibliographic record
Abstract
This study aims at analysis and explanation of the phenomenon of the Islamic terrorism from a cultural perspective; i.e. a cultural reading of the religious violence phenomenon, and how the cultural systems would view the religion-induced use of violence at various levels. Generally, the unique nature of a culture sustains as well as revitalizes certain variants of extremism and terrorism.In the present study, the hypothesis was that the prevalent sociopolitical culture dominating the thinking of the majority of Arab Muslims, which is originally inspired by the religious and historical repertoire, was one of the most effective mechanisms producing extremisms and terrorism. Hence, to stand up to terrorism most effectively, the overall thinking shall be revolutionized towards a civic culture drawing on which to establish the critical mindset based upon novel social values that are capable to interaction with the most recent breakthroughs of the contemporary civilization of the globe.The researcher, as a consequence, recommends reproduction of the current Muslims' culture by employing the educational and media institutions to disseminate an open-minded civic culture that tolerates with others, and accepts multiculturalism and diversity. To that end, the researcher adopted the Content Analysis approach to analyze the content of the dominating culture and its relation to the production of terrorism and ways of confrontation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".