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Gender discrimination is very much an issue in academia generally and in astronomy specifically. Through machine learning techniques, astronomy papers authored by women are shown to have 10% systematically fewer citations than those authored by men.
Using a large-scale analysis of publication records and a random-walk model, Jia and colleagues show that the evolution of scientists’ research interests throughout their careers is characterized by a regular and reproducible pattern.