A review of the rural-digital policy agenda from a community resilience perspective

© 2016 The Authors This paper utilises a community resilience framework to critically examine the digital-rural policy agenda. Rural areas are sometimes seen as passive and static, set in contrast to the mobility of urban, technological and globalisation processes (Bell et al., 2010). In response to notions of rural decline (McManus et al., 2012) rural resilience literature posits rural communities as ‘active,’ and ‘proactive’ about their future (Skerratt, 2013), developing processes for building capacity and resources. We bring together rural development and digital policy-related literature, using resilience motifs developed from recent academic literature, including community resilience, digital divides, digital inclusion, and rural information and communication technologies (ICTs). Whilst community broadband initiatives have been linked to resilience (Plunkett-Carnegie, 2012; Heesen et al., 2013) digital inclusion, and engagement with new digital technologies more broadly, have not. We explore this through three resilience motifs: resilience as multi-scalar; as entailing normative assumptions; and as integrated and place-sensitive. We point to normative claims about the capacity of digital technology to aid rural development, to offer solutions to rural service provision and the challenges of implementing localism. Taking the UK as a focus, we explore the various scales at which this is evident, from European to UK country-level

Surviving Maria from Dominica: Memory, Displacement and Bittersweet Beginnings

This paper refracts questions of human 'resilience' through the prism of social relations. Herein, it asks how Caribbean people utilize interpersonal networks, patterns of sociality and kinship relations to mitigate the exigencies of increasingly violent hurricanes. The essay draws on the individual narratives of three Dominicans: a librarian who recollects moments of familial support during hurricane David of 1979; the post-Maria journey of dislocation of a young woman as she ventures through an extended kin network, finding herself adrift in East London, far from loved ones; and a teacher and mother, who finally gets her 'papers' for America - reunited with her husband after years of waiting, yet, forced to leave her mother, father, and siblings at home. These narratives chart the social debris of Maria, while illustrating the ambivalent routes people take to reassemble their lives. In turn, they present kinship togetherness amidst chaos, an uprooted life in waiting, and the sudden acceleration of a long-awaited familial migration. Hence,'resilience' is revealed as something that is ethnographically fraught with contradiction; ever incomplete and bittersweet. More broadly, the paper complicates questions of 'resilience' by offering an interpersonal ethnographic perspective that compliments the large-scale focus of most disaster scholarship

Quantifying resilience to flooding among households and local government units using system dynamics: a case study in Metro Manila

A generic systems dynamics (SD) model template for resilience is adapted to analyse flooding impacts on household assets and local government assets of Pasig City, Metro Manila. SD simulations are used to quantify the loss of system performance due to adverse impacts, and the recovery of the system due to response measures. The simulation results reflect the decreasing levels of resilience among low‐income households, and the reliance of local government on budgeting cycles to replenish assets. The initial model needs to be expanded to include other determinants of resilience, but this exploratory study reflects the potential usefulness of SD simulations as a decision support tool for city policy makers. By quantifying changes in resilience measures over time, simulations can complement qualitative analyses and test policy and programme scenarios

Predicting Sense of Community and Participation by Applying Machine Learning to Open Government Data

Community capacity is used to monitor socioeconomic development. It is composed of a number of dimensions that can be measured to understand issues possibly arising in the implementation of a policy or of a project targeting a community. Measuring these dimensions is thus highly valuable for policymakers and local administrator, though expensive and time consuming. To address this issue, we evaluated their estimation through a machine learning technique—Random Forests—applied to secondary open government data and determined the most important variables for prediction. We focused on two dimensions: sense of community and participation. The variables included in the data sets used to train the predictive models complied with two criteria: nationwide availability and sufficiently fine-grained geographic breakdown, that is, neighborhood level. Our resultant models are more accurate than others based on traditional statistics found in the literature, showing the feasibility of the approach. The most determinant variables in our models were only partially in agreement with the most influential factors for sense of community and participation according to the social science literature consulted, providing a starting point for future investigation under a social science perspective. Moreover, due to the lack of geographic detail of the outcome measures available, further research is required to apply the predictive models to a neighborhood level