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Reshaping Attention and Inclusion Strategies for Distinctively vulnerable people among the forcibly displaced
Funder: European CommissionProject code: 822688 Call for proposal: H2020-SC6-MIGRATION-2018
Funded under: H2020 | RIA Overall Budget: 2,662,130 EURFunder Contribution: 2,662,130 EUR
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Forced displacement crises overcome societies and institutions all over the world. Pushed by the urgencies rather than events, solutions are frequently reactive, partial, and disregard some groups. The project ‘Reshaping Attention and Inclusion Strategies for Distinctively vulnerable people among the forcibly displaced’ (RAISD) aims at identifying highly Vulnerable Groups (VG) among these forcibly displaced people, analysing their specific needs, and finding suitable practices to address them. The concept of ‘vulnerability context’ considers the interplay between the features of these persons and their hosting communities, their interactions and experiences, and how different solutions for attention and inclusion affect them. As a result of this work, a methodology to carry out these studies will be developed. These goals are aligned with the call. They pursue characterizing these migrations and developing suitable aid strategies for them. The Responsible Research and Innovation (RRI) frames the project. It proposes that all actors (including civil society) co-design actions, transversely integrates the gender perspective, and supports sustainability. Our research strategy will be based on methodological triangulation (i.e. the combined application of several methodologies). We will implement it through a specific participatory action research approach to fulfil the aim of undertaking advocacy-focused research, grounded in human rights and socio-ecological models. The team will work as a network of units in countries along migration routes. The units will promote the VG people’ involvement, so they can speak with their own voices, gather information, and test practices. Work will rely on a tight integration of Social and Computer Sciences research. Automated learning and data mining will help to provide evidence-based recommendations, reducing a priori biases. A software tool will support collaboration, continuing previous H2020-funded RRI work.

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