Study finds gender bias in invited editorials

Harvard and Elsevier researchers uncover gender bias, publishing findings in JAMA Network

By Ian Evans - February 5, 2020
Bamini presents at Harvard workshop
Bamini Jayabalasingham, PhD, Senior Analytical Product Manager at Elsevier, presents findings from a study she co-authored with Harvard and Elsevier colleagues at the Representation In Academia summit, hosted by the Harvard Data Science Initiative. They later published these findings in JAMA Network. (Photo by Alison Bert)

If you’re a woman conducting research, the odds of being invited to author a commentary are significantly lower than if you’re a man. That was the conclusion of a recent study by researchers at Harvard and Elsevier, published in JAMA Network. The researchers found that women were 21 percent less likely to be invited than men with similar scientific expertise, seniority and publication metrics.

When a journal publishes a paper of particular interest, journal editors will reach out to an expert in the field and ask them to write an editorial about the article, discussing its significance. As the study indicates, there is a gender bias in that process: people invited to write those articles are usually men.

Bamini Jayabalasingham, PhDDr. Bamini Jayabalasingham, Senior Analytical Product Manager at Elsevier and one of the authors of the study, explained:

The reason this matters is that when you’re invited to write an editorial, it’s a visible endorsement of your expertise, and it raises your profile in that area. So, it’s one of the elements that builds up your reputation, and with that comes additional opportunities.


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Getting to the heart of gender bias

To examine the possibility of gender bias in these invited editorials, the team Bamini worked with looked at the authors of invited commentaries published in 2,459 journals from January 1, 2013, through December 31, 2017. One of the first challenges they had to overcome was identifying invited editorials across those two-and-a-half-thousand journals.

They used Scopus – Elsevier’s abstract and citation database – to identify these editorials. Then they set up a proxy to identify whether these editorials cited another piece in the same journal volume and issue. As Bamini explained:

We reasoned that if the article cited another article within the same journal issue and volume, then it was almost certainly an invited editorial because the editor would have provided the cited article to the author. You would always have to reference the article you’re discussing, and there’s not many other situations where you’d know about something in the journal being published.

The team then used the same approach to determine the gender of authors as is used in Elsevier’s gender in research reports. From there, they used a kind of classic epidemiology method because co-authors Francesca Dominici and Emma Thomas are epidemiologists. They used the Elsevier Fingerprint Engine “Expert Look Up” capability to establish who the other, equally qualified experts in the field are.

If I choose a man to write an editorial, and they’re literally the only person in that field with the relevant expertise, that’s not an example of bias. What the fingerprint engine helped us identify was whether there were other, equally qualified people who could have been invited instead. Francesca and Emma then analyzed that data to establish whether there was a bias, and to what degree.

Going beyond ‘who you already know’

Harvard Prof. Francesca Dominici, PhD, moderates a panel at the <a target="_blank" href="https://www.elsevier.com/connect/harvard-and-elsevier-are-using-data-science-to-improve-gender-equality-in-academia?utm_source=EC&utm_campaign=5feb">Representation in Academia Summit</a>. (Photo by Alison Bert)Commenting on the findings, Francesca, Professor of Biostatistics at the Harvard TH Chan School of Public Health and Co-Director of the Harvard Data Science Initiative, said:

I’m personally really proud of the work we’ve done, and part of that is that because of the wealth of bibliometric data that’s available, there’s really no reason to invite scientists to write on a specific topic solely based on who you already know. Elsevier’s Scopus platform has all the information you need to find exactly the right expert, even if they’re not in your personal network.

Francesca noted that the issue is compounded by the fact that once someone becomes known for a topic, partly through these editorials, they become an immediate first choice for the next one.

People say, ‘Well we should invite so-and-so because they’re the person you always hear from,’ when there’s a much broader talent pool. It’s an unconscious bias, but it is a bias. What our paper shows is that there is a much larger number of experts people could be calling on.

In her view it’s an issue that editorial boards can begin addressing with relative speed and ease by challenging themselves to reach beyond the obvious names and raise the profile of other experts:

It’s simple to do a search on a particular group of people using keywords and get the full list of people who would be capable of doing the editorial. It’s a way of pushing past any bias. We focused on gender because it was a little easier with the data we had available, but I think in general it’s a way of generating an opportunity for talented people to express their voice, which they otherwise would have never had.


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Contributors


Written by

Ian Evans

Written by

Ian Evans

Ian Evans is Content Director for Global Communications at Elsevier. Previously, he was Editor-in-Chief of Elsevier’s Global Communications Newsroom. Based in Oxford, he joined Elsevier six years ago from a small trade publisher specializing in popular science and literary fiction.

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