|5 Jun 2017||2:00pm - 4:00pm||Seminar Room SG1, Alison Richard Building|
Free and open to all but Online Registration is required. Limited places.
Dr Jennifer Gabrys (Goldsmiths)
Citizen sensing technologies present the possibility for making environmental monitoring technologies and practices available to a wider array of participants. Yet within this so-called democratization of environmental monitoring practices, a key question emerges as to how the democratization of environmental data could also develop. The democratization of data practices could generate alternative approaches to data content and analysis. At the same time, these altered data practices could generate different modes of evidence for making claims and effecting political change by corroborating and combining data with a range of data types, including observations and experience. In this presentation, I will look more closely at the citizen data and data practices that emerged through Citizen Sense research, together with the modes of data interpretation that developed through processes of air quality monitoring, and consider how these practices presented alternative strategies for making accounts of environmental problems and generating evidence. I will consider how these data practices tell different data stories that go beyond the usual uses of environmental data for regulation, compliance and modelling to generate expanded data citizenships, and environmental collectives.
Jennifer Gabrys is Reader in Sociology at Goldsmiths, University of London, and Principal Investigator on the European Research Council funded project, Citizen Sense. She is the author of Digital Rubbish: A Natural History of Electronics (University of Michigan Press, 2011) and Program Earth: Environmental Sensing Technology and the Making of a Computational Planet (University of Minnesota Press, 2016), and the co-editor of Accumulation: The Material Politics of Plastic (Routledge, 2013). Her work can be found at citizensense.net and jennifergabrys.net.
Part of the Ethics of Big Data Research Group Series
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