Machine-learning algorithm quantifies gender bias in astronomy

From Nature:

Citation rates in astronomy are stacked against women, a study that uses machine learning to quantify bias has found.

Researchers from the Swiss Federal Institute of Technology in Zurich, Switzerland, estimate that, as a result of gender bias, papers whose first authors are women receive around 10% fewer citations than do those that are first-authored by men.

Gender disparities in citation patterns have been documented across science before. But researchers have not previously tried to quantify how much of the differences are the result of gender bias. For instance, men and women may publish different types of papers; women may work in different scientific fields, and may hold less-senior positions.

But the new paper, which has not yet been peer-reviewed and was posted on the arXiv preprint server on 27 October1, tries to account and correct for these factors. The authors declined to comment on the paper, because they hope to submit it to Nature Astronomy. But other specialists say that the analysis seems solid.

“The novelty of this paper is in dispelling the myth that gender disparity in citation can be attributed to specifics of the paper, rather than to gender,” says Cassidy Sugimoto, an informaticist at Indiana University Bloomington. Sugimoto has also published work on gender bias in science publications2, and says that the paper’s findings are “at once both terrible and terrific”.

For their study, the researchers analysed 200,000 papers in 5 journals from 1950 to 2015. First, they trained a machine-learning algorithm to accurately calculate the citations for each paper first-authored by a man using as many non-gender-related factors as possible — such as the journal, field and year in which the paper was published, where the first author was located and for how many years that author had been publishing.

Then they unleashed their algorithm on the papers …

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