Speaker: Jiajun Zou, Ph.D. Candidate in History, Emory University

Jiajun Zou introduces a fresh perspective to Ming dynasty studies with his pioneering dataset of 92,000 juren profiles. In his presentation, Zou will share his journey in assembling this comprehensive dataset, utilizing a mix of CBDB resources, computational techniques, and prompt engineering via ChatGPT. He will then present statistical and macro-level evidence of a geographical bias within the examination system, underscoring how proximity to examination centers at both the provincial and national levels significantly impacted odds of success. Zou contends that the examination system displayed a clear proximity bias, favoring those closer to central hubs in terms of outlasting and exhausting their group competitors over time. The challenges faced by peripheral prefectures and regions are attributed not to a lack of talent—as some of the most distant prefectures produced the highest number of juren in China but only a handful of jinshi. Moving beyond the narratives of educational and intellectual traditions, Zou explores whether rising costs of competition, influenced by geographical and social dynamics, shifted the balance of political power in Ming China. This presentation aims to highlight the value of a macro analytical approach using a large dataset to reveal hidden trends and to encourage modern scholars to independently tackle research challenges with innovative digital techniques.

Also via Zoom. Reigster: https://bit.ly/exam-luck

An event sponsored by China Biographical Database Project (CBDB) and Digital China Initiative (DCI), Harvard University