Sino-US S and T Frictions and Transnational Knowledge Flows: Evidence from machine learning and cross-national patent data
원문 초록
This paper identifies the impact of China-U.S. science and technology (S&T) friction on knowledge flows in different fields, using data on invention patent applications from China, the U.S., Europe, and the World Patent Office (WPO) along with machine-learning-based econometric methods. The empirical results find that the negative impacts of China-U.S. S&T frictions on cross-border knowledge flows are confined to a limited number of technology areas during the period of our observation. This paper further explores the characteristics of the negatively impacted technology areas, and the empirical results show that technology areas that rely more on basic scientific research, where the distribution of U.S. scientific and technological strength is more concentrated, and where the gap between U.S. and Chinese science and technology is narrower, are more likely to be the victims of the Sino-U.S. S&T friction.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Frictional Agent Alignment Framework: Slow Down and Don't Break Things
Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction
PyScrew: A Comprehensive Dataset Collection from Industrial Screw Driving Experiments
Barrier-Free Microhabitats: Self-Organized Seclusion in Microbial Communities
PUGS: Zero-shot Physical Understanding with Gaussian Splatting
Know Your Mistakes: Towards Preventing Overreliance on Task-Oriented Conversational AI Through Accountability Modeling
토론
이 논문과 연결된 포럼 글이 아직 없습니다.