Discrete power law with exponential cutoff and Lotka's Law

TL;DR

This paper revisits Lotka's law of author productivity by comparing how well a discrete power law and a power law with exponential cutoff fit bibliometric data, offering insights into the distribution's nature.

Contribution

It introduces a model combining a discrete power law with an exponential cutoff to better fit author productivity data compared to traditional power law models.

Findings

The power law with exponential cutoff provides a better fit to Lotka's data.

Traditional power law models may overestimate the frequency of highly productive authors.

The study offers a refined understanding of author productivity distribution.

Abstract

The first bibliometric law appeared in Alfred J. Lotka's 1926 examination of author productivity in chemistry and physics. The result is that the productivity distribution is thought to be described by a power law. In this paper, Lotka's original data on author productivity in chemistry is reconsidered by comparing the fit of the data to both a discrete power law and a discrete power law with exponential cutoff.