Systematic Review on the Outcomes of Primary and Secondary Prevention Programs in the Field of Violent Radicalization
Bibliographic record
Abstract
Since 2001, attacks attributed to extremist movements or “lone actors” have intensified and spread around the world, prompting governments to invest significant sums of money into preventing violent radicalization. Nonetheless, knowledge regarding best practices for prevention remains disparate, and the effectiveness of current practices is not clearly established. Consequently, we conducted a systematic review on the outcomes of primary and secondary prevention programs in the field of violent radicalization. Of the 11,836 documents generated, 33 studies published between 2009 and 2019 were eligible for inclusion as they comprised an empirical (quantitative or qualitative) evaluation of a prevention initiative using primary data. The majority of these studies evaluated programs targeting violent Islamist or “general” radicalization. Negative or iatrogenic effects mostly stemmed from programs aimed at specific ethnic or religious groups or focusing on surveillance and monitoring. Positive effects were noted in programs aimed at improving potential protective factors against violent radicalization. However, the reviewed studies had numerous limitations (i.e., weak experimental designs, small/biased samples, unclear definitions, incomplete methodological sections, and conflicts of interests) that hinder one’s confidence in their conclusions. Also, many studies lacked a logic model, failed to differentiate between intermediate and final outcomes, and often did not assess for negative outcomes. Encouragingly, however, some of the most methodologically sound studies contained results attesting to the effectiveness of improving protective factors against violent radicalization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".