Medical theses and derivative articles: dissemination of contents and publication patterns

Mercedes Echeverria (), David Stuart () and Tobias Blanke ()
Additional contact information
Mercedes Echeverria: Library of the Universidad Autonoma de Madrid
David Stuart: King’s College London
Tobias Blanke: King’s College London

Scientometrics, 2015, vol. 102, issue 1, No 30, 559-586

Abstract: Abstract Doctoral theses are an important source of publication in universities, although little research has been carried out on the publications resulting from theses, on so-called derivative articles. This study investigates how derivative articles can be identified through a text analysis based on the full-text of a set of medical theses and the full-text of articles, with which they shared authorship. The text similarity analysis methodology applied consisted in exploiting the full-text articles according to organization of scientific discourse IMRaD (Introduction, Methodology, Results and Discussion) using the TurnItIn plagiarism tool. The study found that the text similarity rate in the Discussion section can be used to discriminate derivative articles from non-derivative articles. Additional findings were: the first position of the thesis’s author dominated in 85 % of derivative articles, the participation of supervisors as coauthors occurred in 100 % of derivative articles, the authorship credit retained by the thesis’s author was 42 % in derivative articles, the number of coauthors by article was 5 in derivative articles versus 6.4 coauthors, as average, in non-derivative articles and the time differential regarding the year of thesis completion showed that 87.5 % of derivative articles were published before or in the same year of thesis completion.

Keywords: Derivative articles; Cluster analysis methodology; Medical theses; Coauthorship; Theses supervisors (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11192-014-1442-0

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