Date of Award
Department of Computer Science
This paper proposes a novel framework of predicting future technological change. Using abstracts of academic publications available in the Microsoft Academic graph, co-occurrence matrices are generated to indicate how often occupation and technological terms are referenced together. This matrices are used in linear regression models to predict future co-occurrence of occupations and technologies with a relatively high degree of accuracy as measured through the mean squared error of the models. While this work is unable to link the co-occurrences found in academic publications to automation in the labor force due to a dearth of automation data, future work conducted when such data is available could apply a similar approach with the aim of predicting automation from trends in academic research and publications.
Doty, Elena A., "Mining Academic Publications to Predict Automation" (2020). Dartmouth College Undergraduate Theses. 158.