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Scientometrics

, Volume 117, Issue 1, pp 351–380 | Cite as

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

  • Dominik P. Heinisch
  • Guido Buenstorf
Article

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 

JEL Classification

PI23 O30 D83 D85 

Notes

Acknowledgements

We would like to thank two anonymous reviewers of the ISSI 2017 conference, as well as two reviewers of this journal, for their helpful comments. This work was funded by the German Federal Ministry of Education and Research (BMBF) in its program “Forschung zu den Karrierebedingungen und Karriereentwicklungen des Wissenschaftlichen Nachwuchses (FoWiN)” under Grant 16FWN001.

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Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2018

Authors and Affiliations

  1. 1.Institute of Economics and INCHER KasselUniversity of KasselKasselGermany
  2. 2.Institute of Innovation and EntrepreneurshipUniversity of GothenburgGothenburgSweden
  3. 3.IWH Leibniz Institute of Economics HalleHalleGermany

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