Religiously Inspired Violent Radicalisation in Southern Europe: Why It Is Not Emerging
Bibliographic record
Abstract
Abstract Following 9/11 and the terrorist attacks in western Europe over the past two decades, research on religiously inspired or attributed violent radicalisation has grown into a field of study that has developed a broad and sophisticated range of explanations, theories, and categorisations about violent attacks that are either claimed by groups linked to Islamist ideologies or individuals inspired by them. Within this field, measuring the impact of programmes designed to prevent and counter violent extremism (P/CVE) has attracted increasing interest. While significant research in this field exists in countries that have suffered the most symbolic and mediatised attacks, there is less research being conducted on countries hosting significant Muslim communities of recent arrival such as Italy, Greece, and Spain where there have either been no or very few religiously inspired violent attacks (Italy and Greece) or where such attacks have not triggered the same securitised response (Spain) as elsewhere. Bringing together the three country studies presented in this book—as well as our desk research and contribution to those—this chapter introduces the rationale for this southern European comparative analysis, presents our methodology, and outlines the contents of the chapters that follow.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".