Potential Predictibility of References in the Identification of Derivative Articles from Doctoral Theses
| dc.contributor.author | Echeverria, Mercedes | |
| dc.contributor.author | Stuart, David | |
| dc.contributor.author | Blanke, Tobias | |
| dc.date.accessioned | 2015-03-11T10:15:02Z | |
| dc.date.available | 2015-03-11T10:15:02Z | |
| dc.date.issued | 2015-03-12 | |
| dc.description.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. | en_US |
| dc.identifier.isbn | 978-93-81232-05-7 | |
| dc.identifier.uri | http://hdl.handle.net/1944/1856 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | INFLIBNET Centre | en_US |
| dc.subject | Derivative Articles | en_US |
| dc.subject | Doctoral Theses | en_US |
| dc.subject | Cluster Analysis Methodology | en_US |
| dc.title | Potential Predictibility of References in the Identification of Derivative Articles from Doctoral Theses | en_US |
| dc.type | Article | en_US |