Managing through Closed Loops: the (Im)Possibility of Scale in China's Data-driven Governance

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

This paper examines what we call the (im)possibility of scale in China’s data-driven governance: while the promises of volume and growth of data suggest the possibility of governance at scale, our ethnography shows that it is impossible to realize in practice. Instead, on-the-ground coordination of care and data labor for the data infrastructure still sustains the possibility and fantasy of scale. We illustrate this seeming contradiction through our ethnographic research on the design and implementation of “closed-loop management” for smart aging in place in Shanghai—a system designed to provide big data-informed, reliable care for older adults at home. Theoretically and methodologically, we contribute to CSCW scholarship on scale and scaling by challenging taken-for-granted scalar categories and centering data, coordination, and institutions as key sites through which power relations are negotiated in the processes of scale-making. By turning to the “burden” of scaling, we problematize the assumption of the large scale of data collection in China’s governance as necessarily effective.