ABSTRACT
This systematic review consists of two parts. Part I seeks to synthesise evidence from primary or secondary research studies examining the implementation and effectiveness of case management tools and approaches currently being used to counter radicalisation to violence. Part II is an 'overview of reviews' that seeks to identify relevant and transferable lessons from systematic reviews and meta-analyses of case management tools and approaches used in the broader field of violence prevention that could be applied to counter-radicalisation practice.
ABSTRACT
Several thousand people die every year worldwide because of terrorist attacks perpetrated by non-state actors. In this context, reliable and accurate short-term predictions of non-state terrorism at the local level are key for policy makers to target preventative measures. Using only publicly available data, we show that predictive models that include structural and procedural predictors can accurately predict the occurrence of non-state terrorism locally and a week ahead in regions affected by a relatively high prevalence of terrorism. In these regions, theoretically informed models systematically outperform models using predictors built on past terrorist events only. We further identify and interpret the local effects of major global and regional terrorism drivers. Our study demonstrates the potential of theoretically informed models to predict and explain complex forms of political violence at policy-relevant scales.
