EPISODE · Oct 10, 2024 · 1H 11M
Retrofitting Leninism and Re-examining Hawkishness in China with Dimitar Gueorguiev
from Sinica Podcast
This week, a show taped live at Syracuse University on September 30 with Associate Professor Dimitar Gueorguiev, author of the excellent Retrofitting Leninism: Participation Without Democracy in China. We discuss his book, his recent paper exploring hawkishness in Chinese public opinion, and his thoughts about the upcoming U.S. presidential election.1:59 Syracuse University’s MAX 132 class ("the globalization class")4:10 Dimitar’s background and how he became interested in China 7:44 How the genre of authoritarian resilience took off 14:26 China’s understanding of democracy (whole-process democracy)17:40 Features of Leninism that have allowed the Chinese Communist Party to survive21:21 Why China in the 1980s and '90s admired Singaporea's authoritarian PAP 23:37 The idea of the mass line27:16 China’s sentiment analysis through technology, and using bottom-up information as performance evaluation 34:03 The COVID-19 pandemic and the confirmation bias of the regime-type explanation37:37 The National People’s Congress and the Chinese People’s Political Consultative Conference (CPPCC)40:14 Dimitar’s research on hawkishness in China: how he got the data, what drives Chinese hawkishness, and the national security vs. economic lens 51:08 Why those who are dissatisfied with the government lean more hawkish and those who are satisfied with the government lean more dovish 56:30 The upcoming U.S. election: how things may play out under the two different administrations, and understanding Chinese preferences Recommendations:Dimitar: The TV series The Expanse (2015-2022)Kaiser: Anthea Roberts’ Six Faces of Globalization: Who Wins, Who Loses, and Why It Matters; and the documentary Wise Guy: David Chase and The Sopranos (2024)See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
What this episode covers
This week, a show taped live at Syracuse University on September 30 with Associate Professor Dimitar Gueorguiev, author of the excellent Retrofitting Leninism: Participation Without Democracy in China. We discuss his book, his recent paper exploring hawkishness in Chinese public opinion, and his thoughts about the upcoming U.S. presidential election.1:59 Syracuse University’s MAX 132 class ("the globalization class")4:10 Dimitar’s background and how he became interested in China 7:44 How the genre of authoritarian resilience took off 14:26 China’s understanding of democracy (whole-process democracy)17:40 Features of Leninism that have allowed the Chinese Communist Party to survive21:21 Why China in the 1980s and '90s admired Singaporea's authoritarian PAP 23:37 The idea of the mass line27:16 China’s sentiment analysis through technology, and using bottom-up information as performance evaluation 34:03 The COVID-19 pandemic and the confirmation bias of the regime-type explanation37:37 The National People’s Congress and the Chinese People’s Political Consultative Conference (CPPCC)40:14 Dimitar’s research on hawkishness in China: how he got the data, what drives Chinese hawkishness, and the national security vs. economic lens 51:08 Why those who are dissatisfied with the government lean more hawkish and those who are satisfied with the government lean more dovish 56:30 The upcoming U.S. election: how things may play out under the two different administrations, and understanding Chinese preferences Recommendations:Dimitar: The TV series The Expanse (2015-2022)Kaiser: Anthea Roberts’ Six Faces of Globalization: Who Wins, Who Loses, and Why It Matters; and the documentary Wise Guy: David Chase and The Sopranos (2024) See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
NOW PLAYING
Retrofitting Leninism and Re-examining Hawkishness in China with Dimitar Gueorguiev
No transcript for this episode yet
Similar Episodes
Mar 26, 2026 ·1m
Jan 2, 2026 ·47m
Dec 21, 2025 ·46m