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132 Chapter 4

Box 4.5
A vignette: University blue book

A large North American university contacted its library school to help in developing a blue
book — a database of research expertise present at the university. The objective was to
provide the Donor Relations Group, the Media Group, and the Technology Transfer Group
with a good central reference tool that would enable them to contact the most appropriate
researcher quickly with respect to each of their needs: to present their research to a group
of potential philanthropists (for the Donor Relations Group), to fi nd someone who can
answer questions from the media regarding a current event (for the Media Group), and to
meet with prospect companies interested in commercializing some of the results of their
research (for the Technology Transfer Group). While a number of researcher profi les
existed, they tended to be scattered over personal Web sites, university departmental Web
pages, and other stand-alone applications. The challenge was how to present the same
research to three different target audiences, each with their own preferred terminology.
The library science students quickly set up meetings with representative users from
each of the three groups and conducted card sorting and affi nity diagramming workshops
with each. Existing research profi les and existing commercial taxonomies provided the
terms to be placed on the preprinted cards. The multifaceted taxonomy was the result
with an extensive thesaurus. The database captured the three different perspectives (four
really, counting the researcher ’ s preferred terminology and groupings). Each user group
became a facet and users could search the database using their own specifi c perspective
and their own specialized language.
For example, educational researchers work on social cognition and emotional intelli-
gence (terms used by the researchers themselves) issues to better understand the anteced-
ents of peer pressure and bullying. A cyber-bullying incident brings reporters to call the
Education Department to fi nd someone to speak on the topic ( Kowalski, Limber, and
Agatston 2008 ). Cyber-bullying is a term that has been popularized by the media. The
Donor Relations group showcases some of the research being done to target adolescents
to garner the interest of potential philanthropists who have expressed specifi c interest in
this age group. Finally, a computational linguistics company that has already done some
work in identifying online hate literature is interested in adapting their software to identify
instances of cyber-bullying. This small specialized fi eld of research has rapidly generated
at least eight different but related tags: social cognition, emotional intelligence, peer pres-
sure, bullying (a subgroup of peer pressure), cyber-bullying (a subgroup of bullying),
adolescent behaviors, online hate literature, and computational linguistics. The database
can easily substitute equivalent terms to better respond to the information seeker ’ s needs
and to better adapt to the terms they are more familiar with.