Prevention of Radicalization for Muslims: Concept, Practice, Prospects
Bibliographic record
Abstract
The article examines the concept, practice and prospects for the prevention of radicaliza-tion of Muslims. The purpose of this work is to highlight the general features of the prevention of radicalization of Muslims in the first quarter of the 21st century. Studying the practice of pre-venting radicalization among Muslims makes it possible to trace the development of such measures, to identify the areas of common interest at the global and national levels in the fight against modern extremism and to forecast new developments thereof. According to the author, the prevention of radicalization of Muslims is a set of measures aimed at preserving the non-radicalized state of those who are likely to face a radical Islamist ideology and become radical-ized as a result. These are representatives of local communities, young people and prisoners. The prevention of radicalization is primarily associated with information work done by joint ef-forts of government agencies, civil society activists and religious organizations. In the countries of the East, in contrast to the West, state bodies and religious structures are more active in the implementation of preventive measures. The author concludes that the effectiveness of preven-tive measures depends on correctly selected tools to counter the spread of radical ideology for each narrow group in a particular country.
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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 | 0.015 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".