This webinar presents the results of a study on disparities in faculty salaries at American universities. Researchers, Erick Axxe and Peter Choi, both PhD candidates in Ohio State’s Department of Sociology, discuss how they constructed a dataset of salaries and images from various web sources, elaborate on how they detect race and gender from images, and share DCiFR (a graphical user interface made by their team to run those predictive models).
Peter’s research interests include fertility, mortality, and inequality. He uses methods that are broadly bound by computational social science to collect and analyze data for his research. Network analysis, NLP, machine learning, and deep learning are some of the methods he is actively engaged within his research.
Erick has two veins of research. The first lies in the realm of Computational Social Science, in which he analyzes novel web-based data sources to test for racial and gender inequality. The second investigates how institutions shape identity during the transition to adulthood. His work involves a wide range of data sources: surveys, qualitative interviews, and data scraped from the internet.