Neural embeddings of scientific mobility reveal the stratification of institutions in China

Abstract

We are trying to reveal the status of Chinese universities in the global talent circulation system and how university prestige affects mobility patterns in China. Other than geographic distance, we configured a two-dimensional Euclidean space of the neural embeddings of institutions from scientists’ mobility trajectories. To be specific, we acquired the scientific mobility trajectories based on about 3,055,409 authors from 108 thousand affiliations and used the word2vec neural embedding model to encode affiliations into a continuous 100-dimensional embedding space and then further map the 55,298 distinct embeddings into the two-dimensional space. We found that in the current talent circulation system, China is furthest from the scientific center with a closeness of 0.08 and has a median diversity of 0.15 among the top 10 countries with the most affiliations, indicating relatively less communication with other international institutions and moderate diversity of communication patterns within the country. Furthermore, using the regression models, we found that for the universities on China's mainland, the effects of prestige are positive on the volumes of scientific mobility (all pvalues<0.001) and on the uniqueness of universities (all pvalues<0.1), revealing the stratifications of universities in the talent circulation. The interaction of prestige and regional economic status is also discussed in our results. This work gives policy implications to university development, knowledge circulation, and career development in science.