Publications

In this study we propose and evaluate a method to automatically identify the journal publications that are related to a Ph.D. thesis using bibliographical data of both items. We build a manually curated ground truth dataset from German cumulative doctoral theses that explicitly list the included publications, which we match with records in the Scopus database. We then test supervised classification methods on the task of identifying the correct associated publications among high numbers of potential candidates using features of the thesis and publication records. The results indicate that this approach results in good match quality in general and with the best results attained by the “random forest” classification algorithm.

Often, bibliometric mapping studies remain at a very abstract level when assessing the validity or accuracy of the generated maps. In this case study of citation-based mappings of a research specialty, we dig deeper into the topical structures generated by the chosen mapping approaches and examine their correspondence to a sociologically informed understanding of the research specialty in question. Our analysis highlights the variety of types of topical relatedness and epistemic interdependency that citations can stand for. Unless we assume that invasion biology is unique, our analysis suggests that global algorithmic field classification approaches that use citation links indiscriminately may struggle to reconstruct research specialties.