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

Dominik P. Heinisch () and Guido Buenstorf
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Dominik P. Heinisch: University of Kassel

Scientometrics, 2018, vol. 117, issue 1, No 20, 380 pages

Abstract: Abstract 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.

Keywords: Advisor identification; Fecundity; Ph.D. training; Advisor affects; Academic careers; Machine learning (search for similar items in EconPapers)
JEL-codes: D83 D85 O30 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s11192-018-2840-5

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