Publications

This review of the transparency literature has shown the evolution of the field through the prism of bibliographic and methodological data. In doing so, we have shed light into underlying disparities that go in line with arguments of structural problems and historical relations in socio-political contexts. We have highlighted the irony of the field in researching a topic that, due to determinants beyond the control of scholars (though OA policies may slowly generate a structural change), is in itself little transparent. The blurriness of the state of research in transparency and accountability helps us to address the overall debate of academic production in a critical manner, engaging with issues such as publishing and funding.

Collaborative research is becoming increasingly important. By linking competences and experiences, sharing cost-intensive resources and bringing together different disciplinary perspectives, synergy effects are generated and innovative research projects are promoted. However, the collaboration of researchers also involves risks that can be traced back to the framework conditions that characterise the collaboration of the participants. The DEKiF project ("Determinants and Effects of Cooperations in Homogeneous and Heterogeneous Research Associations") examines the implementation of collaborative research within the framework of different sub-studies with the aim of mapping cooperation problems.

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.