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How to avoid the "infrastructural blues"? Studying-while-caring for data stewardship

Bibliographic Data

ID2289796
AuthorsLuis Felipe R Murillo (0000-0003-0563-7783, University of Notre Dame, corresponding author)
Year2024
Volume48
Issue1
Pages36-51
Publication date2024-05-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueAnnals of Anthropological Practice (BOOK_SERIES)
Journal identifiersISSN: 2153-957X • E-ISSN: 2153-9588
PublisherWiley (PUBLISHER • GB)
DOI10.1111/napa.12208
OpenAlexW4390013803
LanguageEN
References cited37

When it comes to climate crisis research, current debates are increasingly thematizing the needs but also the challenges of collaborative, transdisciplinary work. Geophysical characterizations of climate change are increasingly deemed insufficient to respond to the challenges that vulnerable communities face worldwide. In this paper, I describe the work of studying-while-caring for an environmental data infrastructure in order to address this issue. I suggest framing "data management" anthropologically as a question of collective stewardship that is better conceived as a "knowledge infrastructure" (Edwards 2010) instead of a formal approach to automated data curation. To examine the sociotechnical blindspots of data management, I elaborate on the anthropological concept of "infrastructural blues" based on the data engineering work I conducted. For the conclusion, I discuss the concept of "common" as a substitute for "open" technologies and address the broader implications of the proposed shift toward community stewardship and self-determination as guiding practices for socio-environmental data governance

Blues · Business · Corporate governance · Data curation · Data science · Engineering ethics · Environmental resource management · Framing (construction · Knowledge management · Management · Political science · Politics · Public relations · Sociology · Sociotechnical system · Stewardship (theology · Work (physics · Computer Science · Engineering · Environmental DNA in Biodiversity Studies · Mobile Crowdsensing and Crowdsourcing · Research Data Management Practices

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