Maintainers of Stability: The Labor of China's Data-Driven Governance and Dynamic Zero-COVID

被引:2
作者
Chen, Yuchen [1 ]
Sun, Yuling [2 ]
Lindtner, Silvia [1 ]
机构
[1] Univ Michigan, Ann Arbor, MI 48109 USA
[2] East China Normal Univ, Shanghai, Peoples R China
来源
PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2023) | 2023年
基金
美国国家科学基金会;
关键词
maintenance; data-driven; data; governance; data work; China; affect; labor; COVID; CARE;
D O I
10.1145/3544548.3581299
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper examines the social, technological, and emotional labor of maintaining China's data-driven governance broadly, and dynamic zero-COVID management in particular. Drawing on ethnographic research in China, we examine the sociotechnical work of maintenance during the 2022 Shanghai lockdown. This labor included coordinating mass testing, quarantine, and lockdown procedures as well as implementing ad-hoc technological workarounds and managing public sentiments. We demonstrate that, far from being efected from the top down, China's data-driven governance relies on the circumscribed participation of citizens. During Shanghai's lockdown, citizens with relevant expertise helped to maintain technological stability by fxing or programming data systems, but also to ensure the ongoing production of "positive feelings" about social stability through data-driven governance. In so doing, such citizens simultaneously enacted an ambivalent and circumscribed form of agency, and maintained social and by extension political stability. This article sheds light on data-driven governance and political processes of maintenance.
引用
收藏
页数:16
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