Effectiveness of Adaptation and Integration Mechanisms in Prevention of the Dissemination of Ideologies of Extremism and Terrorism among Migrants
Bibliographic record
Abstract
Nowadays, undoubtedly, one of the severely potential hazards threatening societies due to the migration processes is the spread of extremist ideologies of terrorism among immigrants worldwide. The article analyzes the effectiveness of adaptation and integration mechanisms so as to prevent the spread of ideologies of extremism and terrorism among migrants. This paper illustrates that adaptation and integration mechanisms have significant multifunctional capabilities in preventing the spread of ideologies of extremism and terrorism among migrants. Different approaches to solving migration issues are taken into consideration. It is positively proven that for the prevention of manifestations of the ideologies of extremism as well as terrorism amongst migrants, a particular model of cultural integration is required. Where integration is able to provide a high degree of cultural cohesion, the formation of mutual respect between cultures so that new cultures can integrate into the existing unified culture of the country. As a result, effective mechanisms for the adaptation and integration of migrants in order to prevent the spread of ideologies of extremism and terrorism among migrants are proposed.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".