From Empire to Nation-State: War, Emulation, and National Identity in China

Time & Date: 11:30-13:00 December 6 (Friday)

Venue: TxC401

Abstract:

This article investigates when, why, and how national identity emerged in late-developing countries, with a particular focus on China. Our argument centers on the role of war as a catalyst for two distinct psychological mechanisms: enmity (humiliation and other negative emotions) and emulation (learning from adversaries). While conventional wisdom emphasizes enmity, we propose that emulation—a forward-looking approach where latecomers learn about nationhood from their rivals—also plays a crucial role. To test this theory, we analyze two newly compiled datasets from historical newspapers spanning the period 1872–1911, a time of significant transformation for China as it transitioned from the Qing Empire to the Republic. We focus on the use of “Zhongguo” as a marker of national identity and find that wars triggered a substantial increase in the frequency and proportion of references to “Zhongguo” in Chinese newspapers. Further qualitative evidence supports our theory. This study highlights the critical role of war in nation-building and how national identity is fostered through learning from others.

Speaker’s bio:

Haohan Chen is a computational social scientist. His methodological research focuses on developing computational methods for analyzing text and network data from social media. His substantive research focuses on political communication and political behavior under both authoritarian and democratic contexts. He received his Ph.D. in Political Science and M.S. in Statistical Science from Duke University in December 2019. He worked as a postdoctoral fellow with the Center for the Study of Contemporary China at the University of Pennsylvania (2019-2020) and the Center for Social Media and Politics at New York University (2020-2021). He won the APSA Political Communication Section Paul Lazarsfeld Best Paper Award (2020) and the BEST Award for Master’s Research at Duke Statistical Science (2020).