Third party funded individual grant
Start date : 01.04.2026
End date : 30.09.2027
Our contemporary experience of ‘Big Data’ is not unprecedented (Kaplan and di Lenardo 2017). Across the longue durée of Chinese history, we discern at least three distinct episodes (the 4th c. BCE, the 11th c. and 19th c. CE), when new technologies, material infrastructures, and administrative logics produced 'information explosions'. Previously unquantifiable, unrepresentable, and invalid facts came into being. The household register of the early imperial state, the cadasters of the Song bureaucracy, and the statistical portrait of population in the late Qing each exemplified how mundane technical practices of data-making and keeping could crystallize into powerful instruments of statecraft and legitimization.
Scholarship on Chinese statecraft has long acknowledged the centrality of censuses, land surveys, and standardized metrologies to dynastic statecraft (Tomiya 2010; Deng 2011). Yet framing these practices through the analytic of data—understood not as inert numerical traces but as dynamic artifacts embedded in systems of production, processing, and application—opens new avenues for understanding how material mediums, arithmetic algorithms, and labor regimes cohered into authoritative epistemes. While ostensibly anachronistic, 'data' proves to be a powerful heuristic, foregrounding technical processes and their socio-political effects.
We propose a proof-of-concept project, drawing on our prior empirical studies, that aims to open up 'data' as a new field of historical inquiry for Chinese history. Our 'data history' is emphatically not quantitative, data-driven history, but one scrutinizing the technologies, material infrastructures, and expertise and labour shaping data's generation, circulation, and use. It examines how data practices were mutually constituted with social and political formations. Marrying the history of science and technology with critical data studies, we re-examine Chinese history through situated data practices, tracking how distinct data regimes emerged, evolved, decayed, or were dismantled.