The h-index is a number intended to represent both the productivity and the impact of a particular scientist or scholar, or a group of scientists or scholars (such as a departmental or research group). The h-index is an author level metric that attempts to measure both the productivity and citation impact of the publications of a scholar. The h- index serve as an alternative to traditional journal impact factor metrics in the evaluation of the impact of the work of a particular researcher. Only the most highly cited articles contribute to the h-index.
The h-index is calculated by counting the number of publications for which an author has been cited by other authors at least that same number of times. For instance, an h-index of 17 means that the scientist has published at least 17 papers that have each been cited at least 17 times. If the scientist's 18th most cited publication was cited only 10 times, the h-index would remain at 17. If the scientist's 18th most cited publication was cited 18 or more times, the h-index would rise to 18.
The purpose of the h-index is to eliminate outlier publications that might give a skewed picture of a scientist's impact. For instance, if a scientist published one paper many years ago that was cited 9,374 times, but has since only published papers that have been cited 2 or 3 times each, a straight citation count for that scientist could make it seem that his or her long-term career work was very significant. The h-index, however, would be much lower, signifying that the scientist's overall body of work was not necessarily as significant.
Keep in mind that different databases will give different values for the h-index.
This is because each database must calculate the value based on the citations it contains. Since databases cover different publications in different ranges of years, the h-index result will therefore vary. You should also keep in mind that what is considered a "good" h-index may differ depending on the scientific discipline. A number that is considered low in one field might be considered quite high in another field.
There are some dangers that come with the increasing prevalence of H-scores. It is difficult to compare H-scores across fields. H-scores can often be higher in one field (such economics) than another field (such as literary criticism).
Like any citation metric, H-scores are open to manipulation through practices like self-citation and what one of my old colleagues liked to call “citation soviets” (small circles of people who routinely cite each other’s work).
The H-index also strips out any information about author order. The result is that there is little information about whether you published an article in a top journal on your own or whether you were one member of a huge team.
But perhaps the most worrying thing about the rise of H-scores, or any other measure of research productivity or influence for that matter, is they actually strip out the ideas. They allow us to talk about intellectual endeavour without any reference at all to the actual content.
This can create a very strange academic culture where it is quite possible to discuss academic matters for hours without once mentioning an idea. I have been to meetings where people are perfectly comfortable chatting about the ins and outs of research metrics at great length. But little discussion is had about the actual content of a research project.
As this attitude to research becomes more common, aspirational academics will start to see themselves as H-index entrepreneurs. When this happens, universities will cease to be knowledge creators and instead become metric maximisers.
