Can we predict citation counts of environmental modelling papers? Fourteen bibliographic and categorical variables predict less than 30% of the variability in citation counts. (January 2016)

• Record Type: Journal Article
• Authors: Robson, Barbara J.; Mousquès, Aurélie
• Is Part Of: Environmental modelling & software. Volume 75(2016:Jan.)
• Page Start: 94
• Page End: 104
• Publication Date: 2016-01
• Subjects: Scientometrics -- Informetrics -- Bibliometrics -- Citation count -- Equations; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2015.10.007
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: We assessed 6122 environmental modelling papers published since 2005 to determine whether the number of citations each paper had received by September 2014 could be predicted with no knowledge of the paper's quality. A random forest was applied, using a range of easily quantified or classified variables as predictors. The 511 papers published in two key journals in 2008 were further analysed to consider additional variables. Papers with no differential equations received more citations. The topic of the paper, number of authors and publication venue were also significant. Ten other factors, some of which have been found significant in other studies, were also considered, but most added little to the predictive power of the models. Collectively, all factors predicted 16–29% of the variation in citation counts, with the remaining variance (the majority) presumably attributable to important subjective factors such as paper quality, clarity and timeliness. Highlights: 6122 environmental modelling papers were assessed to determine factors associated with high citation counts. Topic modelling identified seven clusters of papers related by subject. We also assessed 5 very highly cited papers to examine why they were cited. Papers containing differential equations received fewer citations than those without. Papers relating to topics that cross disciplinary boundaries received more citations.