Abstract
Fast-and-frugal heuristics are simple strategies that base decisions on only a few predictor variables. In so doing, heuristics may not only reduce complexity but also boost the accuracy of decisions, their speed, and transparency. In this paper, bibliometrics-based decision trees (BBDTs) are introduced for research evaluation purposes. BBDTs visualize bibliometrics-based heuristics (BBHs), which are judgment strategies solely using publication and citation data. The BBDT exemplar presented in this paper can be used as guidance to find an answer on the question in which situations simple indicators such as mean citation rates are reasonable and in which situations more elaborated indicators (i.e., [sub-]field-normalized indicators) should be applied.
Highlights
Bibliometrics are frequently used in research evaluation
The bibliometrics-based decision trees (BBDTs) presented in the following is a tool for making decisions in bibliometrics-based research evaluations
In the process of developing the BBDT, the experience has been that this process was interesting in view of the application by the later user but was interesting for the developer, because he had to think about the evaluation situation, available indicators, evaluation goals, etc
Summary
Peer review and bibliometrics are combined in an informed peer review process. Bibliometrics are considered “to break open peer review processes, and stimulate peers to make the foundation and justification of their judgments more explicit” The term desktop bibliometrics describes the application of bibliometrics by decision makers (e.g., deans or administrators) without involving experts (i.e., scientists) from the evaluated fields (Leydesdorff, Wouters, & Bornmann, 2016). Another characteristic of “desktop bibliometrics” is the application of inappropriate indicators for measuring performance, since bibliometrics experts are not involved. Informed peer review processes and “desktop bibliometrics” exist side by side in the research evaluation landscape
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