Dissertation title: The Structure of Knowledge Diffusion in Sciences and Consequences
Committee: James Evans (chair), Karin Knorr-Cetina, John Levi Martin
My research is centered on understanding the social conditions and processes that lead to the diffusion of knowledge and robust scientific progress by applying computational and statistical methods to large-scale datasets. In my dissertation, I examine the factors influencing the spread of scientific knowledge and the consequences through three distinct studies. The first study investigates the impact of overlapping co-authorships and prior knowledge on the dispersion of estimates reported in clinical trials. The second study explores potential leading signals associated with sudden collapses in scientific attention paid to biomedical research subfields. Then, the final study evaluates the impact of code sharing in machine learning research and the role of machine learning frameworks on the subsequent citation rates. With these studies, my dissertation seeks to combine insights and theories from science studies and the history of sciences with computational methods, such as natural language processing and network embedding modeling.
Donghyun Kang is a Ph.D. candidate at the University of Chicago Sociology Department, affiliated with Knowledge Lab. His academic interest centers on understanding social conditions and processes that lead to robust scientific progress and dissemination of ideas. Donghyun aspires to extend insights from science studies and the history of sciences with natural language processing and network embedding models. Before Chicago, he received a B.A. in Business Administration and M.A. in Sociology at Seoul National University.
Recent Research / Recent Publications
Kang, Donghyun, and James Evans. "Against method: Exploding the boundary between qualitative and quantitative studies of science." Quantitative Science Studies 1, no. 3 (2020): 930-944.
Kang, Donghyun, TaeYoung Kang, and Junkyu Jang. "Papers with code or without code? Impact of GitHub repository usability on the diffusion of machine learning research." Information Processing & Management 60, no. 6 (2023): 103477.