  When scientists study the phenomena they are interested in, they apply sound
methods and base their work on theoretical considerations. In contrast, when
the fruits of their research is being evaluated, basic scientific standards do
not seem to matter. Instead, simplistic bibliometric indicators (i.e.,
publications and citation counts) are, paradoxically, both widely used and
criticized without any methodological and theoretical framework that would
serve to ground both use and critique. Yet, Bornmann and Marewski [1] proposed
such a framework recently. They developed bibliometrics-based heuristics (BBHs)
based on the fast-and-frugal heuristics approach [2] to decision making, in
order to conceptually understand and empirically investigate the quantitative
evaluation of research as well as to effectively train end-users of
bibliometrics (e.g., science managers, scientists). Heuristics are decision
strategies that use part of the available information and ignore the rest. By
exploiting the statistical structure of task environments, they can aid to make
accurate, fast, effortless, and cost-efficient decisions without that
trade-offs are incurred. Because of their simplicity, heuristics are easy to
understand and communicate, enhancing the transparency of decision processes.
In this commentary, we explain several BBHs and discuss how such heuristics can
be employed in practice (using the evaluation of applicants for funding
programs as one example). Furthermore, we outline why heuristics can perform
well, and how they and their fit to task environments can be studied. In
pointing to the potential of research on BBHs and to the risks that come with
an under-researched, mindless usage of bibliometrics, this commentary
contributes to make research evaluation more scientific.
