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Bayesian Evidence Synthesis (BES) is a method that aggregates levels of evidence from multiple studies. BES is highly flexible because it aggregates at the level of hypotheses instead of on the level of data or model parameters (as, for instance, in meta-analysis). It can therefore aggregate results of highly diverse studies  (e.g., with different design, statistical model, and/or variables) as, for instance, obtained from conceptual replication. BES is however still in its infancy. The proposed (methodological) investigations will mature BES towards a broadly applicable tool, that allows for empirical quantification of the generalizability of scientific theories.

The aim of this research project is to understand strategies, practices, experiences and impact of various modalities of grass roots’ resilience against protracted or chronic urban violence and insecurity in Latin America. The main focus is on collective modes of resilience in and by communities within the so-called ‘urban margins’. However, these modes of resilience will be studied in connection to two other dimensions of urban resilience pertinent to communities in the urban margins: urban security governance, and urbanism, that is to say, socio-spatial and socio-material interventions in the urban assemblage.

In the tsunami of new scientific knowledge, imagine developing guidelines for interventions, treatments, or working on any scientifically based study: there’s not enough time to read everything. Solutions from the field of artificial intelligence are typically closed-source, which is problematic in the era of transparency, especially if AI is making decisions. Moreover, most literature searches are biased toward western studies published in English. Therefore, the current project proposes to develop a fully open-source and real-time AI-aided pipeline using inclusive databases.