Publications

Repeated calls for responsible research metrics and professional codes of conduct in evaluative bibliometrics highlight the need to investigate which qualifications and competencies enable a proficient application of bibliometric methods and indicators. Taking competence research as a point of departure, this chapter delineates salient dimensions of professional competence in bibliometric research evaluation by reviewing a selected subset of the literature. The reviewed literature focuses on handbooks, monographs, and scholarly and practitioner articles that introduce theory and methodology of bibliometrics and showcase applications to scholars and practitioners mainly outside of the scientometric research community.

Sanctions for plagiarism, falsification and fabrication in research are primarily symbolic. This paper investigates sanctions for scientific misconduct and their preceding investigation processes as visible and legitimate symbols. Using three different data sources (retraction notices, expert interviews, and a survey of scientists), we show that sanctions for scientific misconduct operate within a cycle of visibility, in which sanctions are highly visible, while investigation and decision-making procedures remain mostly invisible. This corresponds to high levels of acceptance of sanctions in the scientific community, but a low acceptance of the respective authorities. Such a punitiveness in turn exacerbates confidentiality concerns, so that

We conduct an independent cluster validation study on published clustering solutions of a research testbed corpus, the Astro dataset of publication records from astronomy and astrophysics. We extend the dataset by collecting external validation data serving as proxies for the latent structure of the corpus. Specifically, we collect (1) grant funding information related to the publications, (2) data on topical special issues, (3) on specific journals’ internal topic classifications and (4) usage data from the main online bibliographic database of the discipline. The latter three types of data are newly introduced for the purpose of clustering validation and the rationale for using them for this task is set out.