labors by using tools that reveal and quantify academic
genealogy (Russell & Sugimoto, 2009; see also Sugimoto,
chapter 19, this volume). Researchers whose ideas have
commercial significance can draw on diverse indicators of
impact from patent citation data to trade and industry
press coverage. Lewison (2005), for example, describes
five alternatives/complements to conventional citation
indexes that can be used to track the overall diffusion and
impact of biomedical research, namely, references to
research that appear in international standards, national
policy documents, clinical guidelines, textbooks, and
newspapers. The message seems to be that Web of
Science and Scopus do not tell the whole story.
The Web has engendered a variety of corpora, data
types, and, somewhat less concretely, “genres of
invocation” (Cronin, Snyder, Rosenbaum, Martinson, &
Callahan, 1998, p. 1326) that can be mined to reveal
heretofore largely invisible traces of interaction and
influence. Blog posts and tweets about a scholar’s ideas,
two instances of what has been termed “polymorphous
mentioning” (Cronin et al., 1998, p. 1320), can now be
incorporated into the impact portfolio of individuals or
groups alongside established indicators: the Total Impact
web application is an early prototype of what such a
system might look like.
4
We are no longer limited to
capturing data about formal publications and citations (the
scripts and spoors, respectively, in the title of this chapter).
Rather, the evaluator’s net can be cast more widely to
trawl for novel or overlooked indicators—alt(ernative)
metrics to use the term of art (Priem, 2010; see also
chapters 14, 17, and 16, by Priem, Haustein, and Bar-Ilan
et al., respectively, in this volume)—of scholarly