The Distribution of the Asymptotic Number of Citations to Sets of Publications by a Researcher or From an Academic Department Are Consistent With a Discrete Lognormal Model
Jo\~ao A. G. Moreira, Xiao Han T. Zeng, and Lu\'is A. Nunes Amaral

TL;DR
This paper introduces a new bibliometric indicator based on a discrete lognormal model to accurately measure scientific impact, resistant to manipulation, and applicable across career stages and institutional evolution.
Contribution
The paper develops and validates a novel impact indicator grounded in a discrete lognormal distribution, improving robustness and applicability over existing metrics.
Findings
Accurately measures impact across career stages
Resistant to manipulation and bias
Captures impact evolution over time
Abstract
How to quantify the impact of a researcher's or an institution's body of work is a matter of increasing importance to scientists, funding agencies, and hiring committees. The use of bibliometric indicators, such as the h-index or the Journal Impact Factor, have become widespread despite their known limitations. We argue that most existing bibliometric indicators are inconsistent, biased, and, worst of all, susceptible to manipulation. Here, we pursue a principled approach to the development of an indicator to quantify the scientific impact of both individual researchers and research institutions grounded on the functional form of the distribution of the asymptotic number of citations. We validate our approach using the publication records of 1,283 researchers from seven scientific and engineering disciplines and the chemistry departments at the 106 U.S. research institutions classified…
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