Bibliographies in Traditional Chinese Studies



Professor, Chinese Thought

Project Summary

The prime goal of this summer’s project (Bibliographies on Traditional Chinese Studies) was on refining and expanding Dr. Goldin’s Ancient Chinese Civilization: Bibliography on Western Materials of more than 12,000 items, covering the Neolithic Age to the pre-Buddhist era. Much of the project involved identifying missing items and locating them on UPenn’s Franklin Catalog, as well as updating the bibliography via exhaustive searches for recent publications in over 100 journals. Though intensive library work may be considered a chore, this experience showed me how important it is to take the time to develop one’s own bibliography and to be able to understand how the sources inside them were compiled and constructed over time. The second aim of this project was to identify how this bibliography could serve as a research aide in exploring the academic landscape of Traditional Chinese Studies. We accomplished this by utilizing UMass Amherst’s MAchine Learning for LanguagE Toolkit (MALLET). The “topic modelling” function in MALLET allowed us to observe the evolution of the Ancient Chinese Studies’ academic landscape over time (e.g., the transferral of academic “hubs” from Continental Europe to the US, an explosive growth in number of academic sources following the opening of China, shifting interests from Chinese Culture to Ancient Political Philosophy and East-West Philosophical Dialogues). The process of learning how to use a computer program for the first time, as well as observing the results of machine-learning tools, showed me how much of an impact data analysis would have on a field as disparate as History and Traditional Chinese Studies from the STEM field. The final aim was to apply what we observed in the creation of my own bibliography on the Great Wall of China. Though not comprehensive as Dr. Goldin’s, this bibliography is a culmination of a unique experience of learning how a bibliography is created, expanded, and is used to learn about the “academic landscape” of a subject of interest.