Nearly a decade ago, the science community was introduced to the h-index, a proposed statistical measure of the collective impact of the publications of any individual researcher. Of course, any method of reducing a complex data set to a single number will necessarily have certain limitations and introduce certain biases. However, in this paper we point out that the definition of the h-index actually suffers from something far deeper: a hidden mathematical incompleteness intrinsic to its definition. In particular, we point out that one critical step within the definition of h has been missed until now, resulting in an index which only achieves its stated objectives under certain rather limited circumstances. For example, this incompleteness explains why the h-index ultimately has more utility in certain scientific subfields than others. In this paper, we expose the origin of this incompleteness and then also propose a method of completing the definition of h in a way which remains close to its original guiding principle. As a result, our "completed" h not only reduces to the usual h in cases where the h-index already achieves its objectives, but also extends the validity of the h-index into situations where it currently does not.
ASJC Scopus subject areas
- Computer Science Applications
- Library and Information Sciences