IN CANADA, 2012 Appendices - Council of Canadian Academies

10 The State of Science and Technology in Canada, 2012
Migration patterns were also analyzed at the field level to uncover trends in
different areas of research. The method used for this analysis was similar to that
described above. However, this requires associating an AUID with a specific
field of research. One field of research was associated with each AUID using a
combination of the distribution of its papers across fields and a measure of the
kurtosis of the scientific production towards one field. By constructing vectors of
the author’s share of production for each field and by computing the kurtosis of
each of these vectors, AUIDs were selected where the scientific production peaked
around a single field. Using this technique, a large number of the researchers from
the overall analysis presented earlier were assigned a specific field of research.
Migration was then computed at the field level using these samples. This process
resulted in lower numbers of usable AUIDs because some of the AUIDs could
not be assigned to a specific field. To ensure the analysis was sufficiently robust,
only the fields with at least 1,000 AUIDs were used in this analysis. This left
seven fields for which researcher migration data was available: Information and
Communication Technologies; Engineering; Chemistry; Clinical Medicine; Physics
and Astronomy; Biomedical Research; and Enabling and Strategic Technologies.
Cluster Analysis of Research Topics
An analysis of clusters of related scientific research was also conducted for this
study, which provides an alternative approach to relying on traditional discipline
classification systems. Through an analysis of citation patterns, a cluster-based
classification of research topics was produced to supplement more traditional
journal-based classifications of science used for the majority of the bibliometric
analysis in this report. Using an algorithm for the rapid unfolding of communities
in large networks, Science-Metrix clustered 86 per cent of papers in the Scopus
database (i.e., 16.1 million out of 18.8 million papers). These papers were grouped
into about 48,000 clusters of related research, the majority (i.e., 98 per cent) of
which are smaller than 1,000 papers.
To then identify the topical clusters that are interdisciplinary, researchers at
Science‐Metrix computed the interdisciplinarity of each cluster’s respective
papers based on the distribution of all references listed in each paper across the
sub-fields (or disciplines) of the traditional journal-based classification used in
this study. This is measured by defining a reference network and comparing it
to a vector for all scientific sub-fields. (In this study, the reference network is the
citation network of all Scopus papers published between 1996 and 2010). The
vector of a given cluster is composed of the number of papers in the reference list
of all the papers it contains in each sub-field, based on the selected classification.
The result of this calculation is the interdisciplinary indicator for each topical
cluster. As the interdisciplinarity indicator approaches 1.0, the cluster (or topic)