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The ε-index app: a fairer way to rank researchers with citation data

9 11 2020

Back in April I blogged about an idea I had to provide a more discipline-, gender-, and career stage-balanced way of ranking researchers using citation data.

Most of you are of course aware of the ubiquitous h-index, and its experience-corrected variant, the m-quotient (h-index ÷ years publishing), but I expect that you haven’t heard of the battery of other citation-based indices on offer that attempt to correct various flaws in the h-index. While many of them are major improvements, almost no one uses them.

Why aren’t they used? Most likely because they aren’t easy to calculate, or require trawling through both open-access and/or subscription-based databases to get the information necessary to calculate them.

Hence, the h-index still rules, despite its many flaws, like under-emphasising a researcher’s entire body of work, gender biases, and weighting towards people who have been at it longer. The h-index is also provided free of charge by Google Scholar, so it’s the easiest metric to default to.

So, how does one correct for at least some of these biases while still being able to calculate an index quickly? I think we have the answer.

Since that blog post back in April, a team of seven scientists and I from eight different science disciplines (archaeology, chemistry, ecology, evolution & development, geology, microbiology, ophthalmology, and palaeontology) refined the technique I reported back then, and have submitted a paper describing how what we call the ‘ε-index’ (epsilon index) performs.

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Tags: bibliometrics, citations, code, coding, epsilon index, Github, h-index, ε-index, m-index, performance, ranking, RStudio, Shiny, Shiny app
Categories : gender, mathematics, scientific publishing

A fairer way to rank a researcher’s relative citation performance?

23 04 2020

running I do a lot of grant assessments for various funding agencies, including two years on the Royal Society of New Zealand’s Marsden Fund Panel (Ecology, Evolution, and Behaviour), and currently as an Australian Research Council College Expert (not to mention assessing a heap of other grant applications).

Sometimes this means I have to read hundreds of proposals made up of even more researchers, all of whom I’m meant to assess for their scientific performance over a short period of time (sometimes only within a few weeks). It’s a hard job, and I doubt very much that there’s a completely fair way to rank a researcher’s ‘performance’ quickly and efficiently.

It’s for this reason that I’ve tried to find ways to rank people in the most objective way possible. This of course does not discount reading a person’s full CV and profile, and certainly taking into consideration career breaks, opportunities, and other extenuating circumstances. But I’ve tended to do a first pass based primarily on citation indices, and then adjust those according to the extenuating circumstances.

But the ‘first pass’ part of the equation has always bothered me. We know that different fields have different rates of citation accumulation, that citations accumulate with age (including the much heralded h-index), and that there are gender (and other) biases in citations that aren’t easily corrected.

I’ve generally relied on the ‘m-index’, which is simply one’s h-index divided by the number of years one has been publishing. While this acts as a sort of age correction, it’s still unsatisfactory, essentially because I’ve noticed that it tends to penalise early career researchers in particular. I’ve tried to account for this by comparing people roughly within the same phase of career, but it’s still a subjective exercise.

I’ve recently been playing with an alternative that I think might be a way forward. Bear with me here, for it takes a bit of explaining. Read the rest of this entry »

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Tags: bibliometrics, citations, h-index, m-index, performance, ranking
Categories : conservation, gender, mathematics, science, scientific publishing

Hate journal impact factors? Try Google rankings instead

18 11 2013

A lot of people hate journal impact factors (IF). The hatred arises for many reasons, some of which are logical. For example, Thomson Reuters ISI Web of Knowledge® keeps the process fairly opaque, so it’s sometimes difficult to tell if journals are fairly ranked. Others hate IF because it does not adequately rank papers within or among sub disciplines. Still others hate the idea that citations should have anything to do with science quality (debatable, in my view). Whatever your reason though, IF are more or less here to stay.

Yes, individual scientists shouldn’t be ranked based only on the IF of the journals in which they publish; there are decent alternatives such as the h-index (which can grow even after you die), or even better, the m-index (or m-quotient; think of the latter as a rate of citation accumulation). Others would rather ditch the whole citation thing altogether and measure some element of ‘impact’, although that elusive little beast has yet to be captured and applied objectively.

So just in case you haven’t already seen it, Google has recently put its journal-ranking hat in the ring with its journal metrics. Having firmly wrested the cumbersome (and expensive) personal citation accumulators from ISI and Scopus (for example) with their very popular (and free!) Google Scholar (which, as I’ve said before, all researchers should set-up and make available), they now seem poised to do the same for journal rankings.

So for your viewing and arguing pleasure, here are the ‘top’ 20 journals in Biodiversity and Conservation Biology according to Google’s h5-index (the h-index for articles published in that journal in the last 5 complete years; it is the largest number h such that h articles published in 2008-2012 have at least h citations each):

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