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Group-based Trajectory Modeling (GBTM) is applied to the citation curves ofarticles in six journals and to all citable items in a single field of science(Virology, 24 journals), in order to distinguish among the developmentaltrajectories in subpopulations. Can highly-cited citation patterns bedistinguished in an early phase as "fast-breaking" papers? Can "late bloomers"or "sleeping beauties" be identified? Most interesting, we find differencesbetween "sticky knowledge claims" that continue to be cited more than ten yearsafter publication, and "transient knowledge claims" that show a decay patternafter reaching a peak within a few years. Only papers following the trajectoryof a "sticky knowledge claim" can be expected to have a sustained impact. Thesefindings raise questions about indicators of "excellence" that use aggregatedcitation rates after two or three years (e.g., impact factors). Becauseaggregated citation curves can also be composites of the two patterns,5th-order polynomials (with four bending points) are needed to capture citationcurves precisely. For the journals under study, the most frequently citedgroups were furthermore much smaller than ten percent. Although GBTM has proveda useful method for investigating differences among citation trajectories, themethodology does not enable us to define a percentage of highly-cited papersinductively across different fields and journals. Using multinomial logisticregression, we conclude that predictor variables such as journal names, numberof authors, etc., do not affect the stickiness of knowledge claims in terms ofcitations, but only the levels of aggregated citations (that arefield-specific).

Group-Based Trajectory Modeling of Citations in Scholarly Literature: Dynamic Qualities of "Transient" and "Sticky Knowledge Claims"