A public health approach to understanding and preventing violent radicalization
Bibliographic record
Abstract
BACKGROUND: Very recent acts of terrorism in the UK were perpetrated by 'homegrown', well educated young people, rather than by foreign Islamist groups; consequently, a process of violent radicalization was proposed to explain how ordinary people were recruited and persuaded to sacrifice their lives. DISCUSSION: Counterterrorism approaches grounded in the criminal justice system have not prevented violent radicalization. Indeed there is some evidence that these approaches may have encouraged membership of radical groups by not recognizing Muslim communities as allies, citizens, victims of terrorism, and victims of discrimination, but only as suspect communities who were then further alienated. Informed by public health research and practice, a new approach is proposed to target populations vulnerable to recruitment, rather than rely only on research of well known terrorist groups and individual perpetrators of terrorist acts. CONCLUSIONS: This paper proposes public health research and practice to guard against violent radicalization.
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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 | 0.038 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.001 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.021 | 0.018 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".