Automated citation recommendation tools encourage questionable citations

Serge P J M Horbach, Freek J W Oude Maatman, Willem Halffman and Wytske M Hepkema

Research Evaluation, 2022, vol. 31, issue 3, 321-325

Abstract: Citing practices have long been at the heart of scientific reporting, playing both socially and epistemically important functions in science. While such practices have been relatively stable over time, recent attempts to develop automated citation recommendation tools have the potential to drastically impact citing practices. We claim that, even though such tools may come with tempting advantages, their development and implementation should be conducted with caution. Describing the role of citations in science’s current publishing and social reward structures, we argue that automated citation tools encourage questionable citing practices. More specifically, we describe how such tools may lead to an increase in: perfunctory citation and sloppy argumentation; affirmation biases; and Matthew effects. In addition, a lack of transparency of the tools’ underlying algorithmic structure renders their usage problematic. Hence, we urge that the consequences of citation recommendation tools should at least be understood and assessed before any attempts to implementation or broad distribution are undertaken.

Keywords: citation recommendation tools; research evaluation; citing practices; Matthew effect; bias (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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