Patchwork surveillance and accountability labor: China's Health Code Systems during COVID-19
Contact-tracing systems emerging during the COVID-19 pandemic, represented by China's Health Code Systems (HCSs), are commonly portrayed as top-down, data-driven, seamless, and automatic surveillance systems. In contrast, drawing on science and technology studies (STS) insights, this article examines the practical, open-ended, relational, and bottom-up dynamics of contact-tracing and surveillance systems, focusing on street-level state actors and their accountability labor involved in the HCSs. Based on interviews and documentary analysis conducted between 2020 and 2022 across multiple localities, we reveal that HCSs were less like systems but more like a sociotechnical assemblage that is contingently shaped and maintained by heterogeneous actors, power positions, meanings and interests, actions and performances, and types of labor. In particular, street-level state actors, who have high stakes in the party-state system and face huge top-down accountability pressure, treated the HCSs less as a surveillance tool but more as a tool for practicing, performing, and eluding accountability labor. In turn, via interacting with the HCSs, tech giants, local telecommunication subcontractors, and citizens, they demonstrate their competence in the administrative function, avoid scrutiny and punishment, and protect their political careers. This article paints a realistic picture of conflicts and disconnections within surveillance systems. It highlights the often-overlooked role of street-level state actors in shaping such systems in contingent and unintended ways. Further, it reveals how the surveillance-oriented narratives and assumptions and the practical reality, while misaligned with each other, work together to make citizens passively and actively comply with uncertain and outrageous control.
Authors
- Elise Li Zheng (ORCID: https://orcid.org/0000-0002-5279-8945)
- Jack Linzhou Xing (ORCID: https://orcid.org/0009-0003-5843-1751)
Institutions
- Harvard University Press (US)
- Columbia University (US)
Publication Details
- Journal
- Big Data & Society
- Published
- 2026-02-03
- DOI
- https://doi.org/10.1177/20539517261419359
- Primary Topic
- COVID-19 Digital Contact Tracing
- Type
- article
- Field-Weighted Citation Impact
- 0.00