Model-based evaluation of scientific impact indicators

Abstract

Using bibliometric data artificially generated through a model of citation dynamics calibrated on empirical data, we compare several indicators for the scientific impact of individual researchers. The use of such a controlled setup has the advantage of avoiding the biases present in real databases, and allows us to assess which aspects of the model dynamics and which traits of individual researchers a particular indicator actually reflects. We find that the simple citation average performs well in capturing the intrinsic scientific ability of researchers, whatever the length of their career. On the other hand, when productivity complements ability in the evaluation process, the notorious $h$ and $g$ indices reveal their potential, yet their normalized variants do not always yield a fair comparison between researchers at different career stages. Notably, the use of logarithmic units for citation counts allows us to build simple indicators with performance equal to that of $h$ and $g$. Our analysis may provide useful hints for a proper use of bibliometric indicators. Additionally, our framework can be extended by including other aspects of the scientific production process and citation dynamics, with the potential to become a standard tool for the assessment of impact metrics.

12 pages, 8 figures

References in corpus (5)

• Universality of citation distributions: towards an objective measure of scientific impact
• Diffusion of scientific credits and the ranking of scientists
• Ranking Scientific Publications Using a Simple Model of Network Traffic
• A review of the characteristics of 108 author-level bibliometric indicators
• On the calculation of percentile-based bibliometric indicators

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• Geography of Science: Competitiveness and Inequality
• Randomizing growing networks with a time-respecting null model
• The long-term impact of ranking algorithms in growing networks
• The Evolution of Competitiveness across Economic, Innovation and Knowledge production activities
• Viewing citation trend of Indian physics and astronomy research papers since 2005 through the lens of some new indicators