Extremism in the Islamic Country and Its Relationship with the International Policy
Bibliographic record
Abstract
Extremism is one of themes that do not comply with all the established beliefs and healthy ideas because man is naturally built on moderation, and not extremism or militancy. The human communities are constituted throughout cooperation to keep the sustainability of life. The extremism happening today, particularly in Islamic countries, is only a result of internal reasons within Muslim societies, and the relationship of the dominant international policies on the capabilities of the peoples. In turn, this led to stick extremism to the Islamic approach and religion, because of the docility of some ignorant people of what is being plotted by those countries' policies and the shortening of some leaders of this matter in general. The present study has used the descriptive analytical approach to these issues. It concludes that there are negative reasons within Islamic countries, such as ignorance, economic and social aspects, Internet channels, some scientists' shortage in the preaching side, and the major role of the international politics in fomenting and supporting extremism in these countries. This role is represented in the unlawful interference, military support, and law legislation which are not suitable with the provisions of the Islamic religion that rejects all kinds of extremism through its legislation used from generation to generation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".