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1. Differentiated Borders of Belonging and Exclusion: European Migrants in Rural Areas in Iceland

   https://doi.org/10.17645/si.7756  

   Skaptadóttir, Unnur Dís; Wojtyńska, Anna; Innes, Pamela (March 2024, Social inclusion)

   This article addresses questions of difference, positionality, and belonging from the perspectives of international migrants living and working in rural communities in Iceland. With the recent integration of rural areas into the global economy, small villages and towns have undergone rapid social transformation. The development of new industries and growing tourism in these localities has attracted many international migrants. The share of migrants in the local populations oscillates between 10% to 50%, depending on the town, with the majority coming from Europe. Commonly, they make up the greater part of workers in service jobs and manual labour in rural towns and villages. This article builds on data from ethnographic field research over 15 months in five parts of Iceland located outside of the capital region. Based on the analysis of interviews with migrants, we examine different perceptions of affinity and belonging and explore their experiences of inclusion and exclusion. To what extent do migrants see themselves as part of local communities? How do they narrate their social positions in those places? The discussion highlights how social stratification and hierarchy affect migrants’ experiences of inclusion as commonly displayed in the interviews. Furthermore, we elaborate on how notions of relatedness and otherness reflect inherited ideas of Europe and contemporary divergent geopolitical positions. 

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2. How local community context shapes labour market re-entry and resource mobilisation among return migrants: an examination of rural and urban communities in Mexico

   https://doi.org/10.1080/1369183X.2020.1758552  

   Wassink, Joshua; Hagan, Jacqueline (January 2020, Journal of Ethnic and Migration Studies)

   Recent estimates suggest that nearly half of all international migrants return to their communities of origin within five years of emigration. Motivated by high levels of return migration, scholars are increasingly investigating the ways in which return migrants mobilise resources they acquire abroad, such as human and financial capital, to achieve economic mobility upon return. Yet, resource mobilisation and labour market reintegration unfold in heterogeneous community contexts. To understand the labour market reintegration of return migrants in various local contexts, we draw on an eight-year study that included interviews with 153 Mexican returnees to examine how labour market reintegration and resource mobilisation vary across three types of communities: urban, urban-adjacent, and rural. U.S.-Mexico migration is the largest binational return flow in the world, providing a unique opportunity to explore variations in the reintegration experiences of returnees. We find that labour market reintegration and resource mobilisation are contextually embedded processes that respond to the social, economic, and spatial features of migrants’ origin communities. Following our analysis, we extend three testable hypotheses that can guide future research on international migration and return. 

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3. Advancing convergence research: Renewable energy solutions for off-grid communities

   https://doi.org/10.1073/pnas.2207754119  

   Moran, Emilio F.; Lopez, Maria Claudia; Mourão, Rachel; Brown, Erik; McCright, Aaron M.; Walgren, Judith; Bortoleto, Ana Paula; Mayer, Adam; Johansen, Igor Cavallini; Ramos, Karina Ninni; et al (December 2022, Proceedings of the National Academy of Sciences)

   Millions of people across the world live off-grid not by choice but because they live in rural areas, have low income, and have no political clout. Delivering sustainable energy solutions to such a substantial amount of the world’s population requires more than a technological fix; it requires leveraging the knowledge of underserved populations working together with a transdisciplinary team to find holistically derived solutions. Our original research has resulted in an innovative Convergence Framework integrating the fields of engineering, social sciences, and communication, and is based on working together with communities and other stakeholders to address the challenges posed by delivering clean energy solutions. In this paper, we discuss the evolution of this Framework and illustrate how this Framework is being operationalized in our on-going research project, cocreating hybrid renewable energy systems for off-grid communities in the Brazilian Amazon. The research shows how this Framework can address clean energy transitions, strengthen emerging industries at local level, and foster Global North–South scholarly collaborations. We do so by the integration of social science and engineering and by focusing on community engagement, energy justice, and governance for underserved communities. Further, this solution-driven Framework leads to the emergence of unique approaches that advance scientific knowledge, while at the same time addressing community needs. 

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4. Rural Smart Shrinkage and Perceptions of Quality of Life in the American Midwest

   https://doi.org/10.1007/978-3-030-50540-0_20  

   Zarecor, K.; Peters, D.; Hamideh, S. (January 2021, International handbooks of qualityoflife)

   null; null; null (Ed.)

   Rural communities in the American Midwest have experienced upheaval since the 1980s with shrinking populations, an exodus of younger people, job losses, and aging infrastructure. Evidence shows that these trends are unlikely to be reversed in most places. Despite this, high-cost and unproven economic growth strategies are still promoted as the most appropriate responses. This chapter presents findings from an interdisciplinary research project about quality of life (QoL) in rural Iowa that proposes changing the analytical paradigm in this context from growth to one that sees adaptation to population loss as a form of resiliency. Using qualitative and quantitative data, the research considers how some shrinking rural communities in Iowa have been better able to adapt than others. The research shows that negative trends in QoL perceptions in these towns were best mitigated by investing in community services and social capital, rather than in economic development. This process is described as rural smart shrinkage, a term borrowed from research on shrinking cities and applied here to rural communities for the first time. The framework shifts away from associating population loss with community decline and instead emphasizes intentional and low-cost strategies that may sustain shrinking rural communities into the future. 

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5. Socially situated artificial intelligence enables learning from human interaction

   https://doi.org/10.1073/pnas.2115730119  

   Krishna, Ranjay; Lee, Donsuk; Fei-Fei, Li; Bernstein, Michael S. (September 2022, Proceedings of the National Academy of Sciences)

   Regardless of how much data artificial intelligence agents have available, agents will inevitably encounter previously unseen situations in real-world deployments. Reacting to novel situations by acquiring new information from other people—socially situated learning—is a core faculty of human development. Unfortunately, socially situated learning remains an open challenge for artificial intelligence agents because they must learn how to interact with people to seek out the information that they lack. In this article, we formalize the task of socially situated artificial intelligence—agents that seek out new information through social interactions with people—as a reinforcement learning problem where the agent learns to identify meaningful and informative questions via rewards observed through social interaction. We manifest our framework as an interactive agent that learns how to ask natural language questions about photos as it broadens its visual intelligence on a large photo-sharing social network. Unlike active-learning methods, which implicitly assume that humans are oracles willing to answer any question, our agent adapts its behavior based on observed norms of which questions people are or are not interested to answer. Through an 8-mo deployment where our agent interacted with 236,000 social media users, our agent improved its performance at recognizing new visual information by 112%. A controlled field experiment confirmed that our agent outperformed an active-learning baseline by 25.6%. This work advances opportunities for continuously improving artificial intelligence (AI) agents that better respect norms in open social environments. 

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