Summary
Citizen science has been motivated by several perspectives, including increased efficiency in data collection and distributed analysis, democratizing knowledge production, making science more responsive to community needs, and improving the representation of marginalized populations in public data. Despite the potential of citizen science to achieve social justice agendas through a data-intensive and data-driven participatory scientific enquiry, scholarship in critical data studies offers several problematizations of data-based practices, highlighting risks of exclusion and inequality. To understand the extent to which citizen science supports and challenges forms of injustice, this study used a “data justice” analytical framework to critically explore the assemblages of citizen science. We examined four citizen science cases with different levels of citizen engagement, intended outcomes, and data systems. The analysis suggests instances of injustice occurring throughout the data processes of the citizen science cases across the dimensions of procedural, instrumental, rights-based, structural, and distributive data justice.
With the information revolution that promises to shape the 21st century, knowledge has become the prime commodity, very much like land, means of production and capital have been at different times in the past. Access to information, made instantly available by the growth of the Internet, determines access to economic resources, social participation and better quality of life. For this reason, the knowledge stored on the Web and the advantages offered by the spread of Information and Communication Technology (ICT) are equally important for rich societies to prosper and for poor ones to develop. The only difference is that marginalised communities do not have access to the tools and have little control over the content found in the domain of ICTs. In this paper we describe an intervention to develop the potential of a typical rural community in South Africa through ICTs. This involves providing Internet connectivity and deploying a platform to support e-commerce, e-learning, e-government and e-health. The core of the platform is an ontology-based model designed to integrate and respond to Indigenous Knowledge Systems. This has been achieved by combining a deep understanding of local knowledge and social networks with the use of authoring, communication and ontology-management tools. The primary goal of this new approach is to find a way to make ICT solutions more sensitive to the local context, and therefore more effective. Secondly, we hope to foster a sense of ownership of the project among the community, by capitalising on local knowledge and resources.
One of the challenges for implementing Sustainable Development Goals (SDGs) is the measurement of indicators that represent progress towards such goals. Measuring such progress enables data-driven decision-making and management of SDG-relevant projects and strategies. The premise of this research is that measuring such indicators depends on measuring so-called means of implementation, i.e. activities that directly contribute to the achievement of SDGs. Building on this premise, this article studies how the measurement of digital government (DG) can contribute to the measurement of SDGs. In particular, how the indicators originating in three DG measurement instruments can inform the SDG indicators. The main finding is an alignment matrix, showing how the DG indicators contribute with varying level of specificity to the measurement of 10 SDG indicators.
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