Comment on “Quantifying long-term scientific impact”

Jian Wang · Yajun Mei · Diana M. Hicks

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scientometrics and bibliometrics research Psychology Research and Bibliometrics Web visibility and informetrics

Abstract

Wang et al. (Reports, 4 October 2013, p. 127) claimed high prediction power for their model of citation dynamics. We replicate their analysis but find discouraging results: 14.75% papers are estimated with unreasonably large μ (>5) and λ (>10) and correspondingly enormous prediction errors. The prediction power is even worse than simply using short-term citations to approximate long-term citations.

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2013Quantifying Long-Term Scientific Impact 2004Sleeping Beauties in science 2012Citation time window choice for research impact evaluation 2003Better late than never? On the chance to become highly cited only beyond the standard bibliometric time horizon

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2014Response to Comment on “Quantifying long-term scientific impact”2021Ranking Papers by their Short-Term Scientific Impact 2021Prediction and application of article potential citations based on nonlinear citation-forecasting combined model

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