The next generation (plus one): an analysis of doctoral students’ academic fecundity based on a novel approach to advisor identification

• Published in: Scientometrics 2018, Vol.117 (1), p.351-380
• Main Authors: Heinisch, Dominik P., Buenstorf, Guido
• Format: Article
• Language: English
• Subjects: Academic careers; Advisor affects; Advisor identification; Advisors; Artificial intelligence; Business Administration; Colleges & universities; Computer Science; Datasets; Economics; Economics and Business; Ekonomi och näringsliv; Electrical engineering; Fecundity; Företagsekonomi; Graduate students; Graduate studies; Information Storage and Retrieval; Learning algorithms; Library Science; Machine learning; Nationalekonomi; Ph.D. training; Scientists; Students
• ISSN: 0138-9130; 1588-2861; 1588-2861
• DOI: 10.1007/s11192-018-2840-5

Scientific communities reproduce themselves by allowing senior scientists to educate young researchers, in particular through the training of doctoral students. This process of reproduction is imperfectly understood, in part because there are few large-scale datasets linking doctoral students to their advisors. We present a novel approach employing machine learning techniques to identify advisors among (frequent) co-authors in doctoral students’ publications. This approach enabled us to construct an original dataset encompassing more than 20,000 doctoral student-advisor pairs in applied physics and electrical engineering from German universities, 1975–2005. We employ this dataset to analyze the “fecundity” of doctoral students, i.e. their probability to become advisors themselves.