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Group-Based Trajectory Modeling of Citations in Scholarly Literature:
Dynamic Qualities of "Transient" and "Sticky Knowledge Claims"
Available at http://arxiv.org/abs/1303.4366 ; ** apologies for
Group-based Trajectory Modeling (GBTM) is applied to the citation curves of
articles in six journals and to citable items--articles, reviews, and
letters--in a single field of science ("virology", 24 journals), in order to
distinguish statistically among the developmental trajectories in
subpopulations. Can highly-cited citation patterns be distinguished in an
early phase as "fast-breaking" papers? Can "late bloomers" or "sleeping
beauties" be considered as a statistically significant groups or are these
uncommon exceptions? GBTM has proved a useful method for investigating
specific citation trajectories.
Most interestingly, the findings raise questions about typical indicators of
"excellence" that use aggregated citation rates after two or three years
(e.g., impact factors). In contrast, we find significant differences between
"sticky knowledge claims" that continue to be cited more than ten years
after publication, and "transient knowledge claims" that show a decay
pattern after reaching a peak within a few years. Although both patterns
exhibit a rapid increase in the first years after publication, only papers
following the trajectory of a "sticky knowledge claim" can be expected to
have a sustained influence. Because findings indicate that aggregated
citation curves can also be composites of the two patterns, 5th-order
polynomials (with four bending points) are needed to capture citation curves
precisely. For the journals under study, the most frequently cited groups
were much smaller than ten percent. However, GBTM did not enable us to
define a percentage of highly-cited papers inductively across different
fields and journals.
Susanne Baumgartner & Loet Leydesdorff
Amsterdam School of Communications Research (ASCoR)
Kloveniersburgwal 48, 1012 CX Amsterdam.
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