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Author Echeverria, Mercedes ♦ Stuart, David ♦ Blanke, Tobias
Source Inflibnet's Institutional Repository
Content type Text
Publisher INFLIBNET Centre
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science ♦ Library & information sciences
Subject Keyword Derivative Articles ♦ Doctoral Theses ♦ Cluster Analysis Methodology
Abstract This paper reports the results obtained on the predictability of references for the identification of derivative articles from doctoral theses, based on a sample of 68 medical theses and 334 articles published by the same theses authors. The study performs an analysis of the common references shared by theses and articles through a text similarity approach. A textual similarity comparison is carried out with the discursive sections of articles (Introduction, Methodology, Results and Discussion) based on the full-text of theses and articles. The results suggest that the Reference section has a high sensitivity to detect true positives cases and a low specificity to identify negative cases, corresponding to a high recall a low precision in the detection of derivative articles.
ISBN 9789381232057
Education Level UG and PG
Learning Resource Type Article