TY - JOUR
T1 - Staying with the trouble of networks
AU - van Geenen, Daniela
AU - Gray, Jonathan W. Y.
AU - Bounegru, Liliana
AU - Venturini, Tommaso
AU - Jacomy, Mathieu
AU - Meunier, Axel
N1 - Funding Information:
The authors would like to thank all co-workers and participants who contributed to or attended the workshops and projects on which this article draws, including at events and during collaborations in Amsterdam, London, New York, Paris, Utrecht, and Siegen. Research on journalistic network practices was initially developed through a project with the Tow Center for Digital Journalism at Columbia University, including JG and LB's exchanges with Bruno Latour on a trip to New York in 2014. We were greatly inspired by discussions with participants at the Networks and their Publics workshop in 2018 supported by the Department of Digital Humanities at King's College London. We are especially grateful for intellectual exchanges with members of the Collaborative Research Center Media of Cooperation (SFB 1187, University of Siegen), whose close reading of and commentary on the article were vital in developing it further. We would also like to thank the MiniVAN development team for fruitful discussions and the colleagues at SAGE for their support in establishing the Public Data Lab as well as for the Concept Grant through which the MiniVAN project was funded.
Funding Information:
The publication of this article was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 262513311—SFB 1187. MiniVAN was published through a SAGE Ocean Concept Grant.
Publisher Copyright:
Copyright © 2023 van Geenen, Gray, Bounegru, Venturini, Jacomy and Meunier.
PY - 2023/1/26
Y1 - 2023/1/26
N2 - Networks have risen to prominence as intellectual technologies and graphical representations, not only in science, but also in journalism, activism, policy, and online visual cultures. Inspired by approaches taking trouble as occasion to (re)consider and reflect on otherwise implicit knowledge practices, in this article we explore how problems with network practices can be taken as invitations to attend to the diverse settings and situations in which network graphs and maps are created and used in society. In doing so, we draw on cases from our research, engagement and teaching activities involving making networks, making sense of networks, making networks public, and making network tools. As a contribution to “critical data practice,” we conclude with some approaches for slowing down and caring for network practices and their associated troubles to elicit a richer picture of what is involved in making networks work as well as reconsidering their role in collective forms of inquiry.
AB - Networks have risen to prominence as intellectual technologies and graphical representations, not only in science, but also in journalism, activism, policy, and online visual cultures. Inspired by approaches taking trouble as occasion to (re)consider and reflect on otherwise implicit knowledge practices, in this article we explore how problems with network practices can be taken as invitations to attend to the diverse settings and situations in which network graphs and maps are created and used in society. In doing so, we draw on cases from our research, engagement and teaching activities involving making networks, making sense of networks, making networks public, and making network tools. As a contribution to “critical data practice,” we conclude with some approaches for slowing down and caring for network practices and their associated troubles to elicit a richer picture of what is involved in making networks work as well as reconsidering their role in collective forms of inquiry.
KW - algorithm studies
KW - critical data practice
KW - data studies
KW - ethnomethodology
KW - feminist epistemologies
KW - science and technology studies
UR - http://www.scopus.com/inward/record.url?scp=85147781729&partnerID=8YFLogxK
U2 - 10.3389/fdata.2022.510310
DO - 10.3389/fdata.2022.510310
M3 - Article
SN - 2624-909X
VL - 5
JO - Frontiers in Big Data
JF - Frontiers in Big Data
M1 - 510310
ER -