H-index - Criticism

Criticism

There are a number of situations in which h may provide misleading information about a scientist's output: (However, most of these are not exclusive to the h-index.)

• The h-index does not account for the number of authors of a paper. In the original paper, Hirsch suggested partitioning citations among co-authors. Even in the absence of explicit gaming, the h-index and similar indexes tend to favor fields with larger groups, e.g. experimental over theoretical.
• The h-index does not account for the typical number of citations in different fields. Different fields, or journals, traditionally use different numbers of citations.
• The h-index discards the information contained in author placement in the authors' list, which in some scientific fields is significant.
• The h-index is bounded by the total number of publications. This means that scientists with a short career are at an inherent disadvantage, regardless of the importance of their discoveries. For example, Évariste Galois' h-index is 2, and will remain so forever. Had Albert Einstein died after publishing his four groundbreaking Annus Mirabilis papers in 1905, his h-index would be stuck at 4 or 5. This is also a problem for any measure that relies on the number of publications. However, as Hirsch indicated in the original paper, the index is intended as a tool to evaluate researchers in the same stage of their careers. It is not meant as a tool for historical comparisons.
• The h-index does not consider the context of citations. For example, citations in a paper are often made simply to flesh out an introduction, otherwise having no other significance to the work. h also does not resolve other contextual instances: citations made in a negative context and citations made to fraudulent or retracted work. This is also a problem for regular citation counts.
• The h-index gives books the same count as articles making it difficult to compare scholars in fields that are more book-oriented such as the humanities.
• The h-index does not account for confounding factors such as "gratuitous authorship", the so-called Matthew effect, and the favorable citation bias associated with review articles. Again, this is a problem for all other metrics using publications or citations.
• The h-index has been found to have slightly less predictive accuracy and precision than the simpler measure of mean citations per paper. However, this finding was contradicted by another study.
• The h-index is a natural number which reduces its discriminatory power. Ruane and Tol therefore propose a rational h-index that interpolates between h and h + 1.
• The h-index can be manipulated through self-citations, and if based on Google Scholar output, then even computer-generated documents can be used for that purpose, e.g. using SCIgen.