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Policies that seek to mitigate poverty by acting on equal opportunity have been found to aggravate discrimination against the poor (aporophobia), since individuals are made responsible for not progressing in the social hierarchy. Only a minority of the poor benefit from meritocracy in this era of growing inequality, generating resentment among those who seek to escape their needy situations by trying to climb up the ladder. Through the formulation and development of an agent-based social simulation, this study aims to analyse the role of norms implementing equal opportunity and social solidarity principles as enhancers or mitigators of aporophobia, as well as the threshold of aporophobia that would facilitate the success of poverty-reduction policies. The ultimate goal of the social simulation is to extract insights that could help inform and guide a new generation of policy making for poverty reduction by acting on the discrimination against the poor, in line with the UN “Leave No One Behind” principle. An “aporophobia-meter” will be developed and guidelines will be drafted based on both the simulation results and a review of poverty reduction policies at regional levels.
Equitable and inclusive quality education is a human right. It is crucial to provide for the learning needs of every child, especially those with learning disabilities. Traditional approaches to learning propose education paths performed with speech therapists. One of the most efficient strategies to help children with reading comprehension difficulties is the creation of a ``concept map'', a structured summary of the written text in a graph structure. Online tools that offer students the possibility to manually create or automatically extract concept maps from text have been created over the years. However, there is still a shortage of software that are specifically designed for children at risk and which produce a concept map that is tailored to the clinical profiles of individuals. In this Project Collaboration, we want to tackle this gap by implementing a multi-modal, online and open-access Artificial-Intelligence powered tool that could help these children to make sense of written text by enabling them to interactively create concept maps. The expected output is threefold. We will implement a new model for concept-map-based document summarization and a clinically appropriate web interface. We will evaluate them in real-world settings through user studies performed by speech therapists.