The h-index is an index that attempts to measure both the productivity and impact of the published work of a scientist or scholar. The index is based on the set of the scientist's most cited papers and the number of citations that they have received in other publications. The index can also be applied to the productivity and impact of a group of scientists, such as a department or university or country, as well as a scholarly journal. The index was suggested by Jorge E. Hirsch, a physicist at UCSD, as a tool for determining theoretical physicists' relative quality[1] and is sometimes called the Hirsch index or Hirsch number.
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The h-index serves as an alternative to more traditional journal impact factor metrics in the evaluation of the impact of the work of a particular researcher. Because only the most highly cited articles contribute to the h-index, its determination is a relatively simpler process. Hirsch has demonstrated that h has high predictive value for whether a scientist has won honors like National Academy membership or the Nobel Prize. The h-index grows as citations accumulate and thus it depends on the 'academic age' of a researcher.
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Hirsch intended the h-index to address the main disadvantages of other bibliometric indicators, such as total number of papers or total number of citations. Total number of papers does not account for the quality of scientific publications, while total number of citations can be disproportionately affected by participation in a single publication of major influence (for instance, methodological papers proposing successful new techniques, methods or approximations, which can generate a large number of citations), or having many publications with few citations each. The h-index is intended to measure simultaneously the quality and quantity of scientific output.
There are a number of situations in which h may provide misleading information
- The h-index does not account for the number of authors of a paper. 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. 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.[16]
- 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.