Author: University of Southern California
Author Contact: usc.edu
Published: 5th Oct 2022
Peer-Reviewed Publication: Yes
Additional References: Gender Equality Publications
Summary: University of Southern California Information Sciences Institute researchers used artificial intelligence to study gender disparities in science.
The glass ceiling refers to a qualified person wishing to advance within their organization's hierarchy being stopped at a lower level due to discrimination, most often based on sexism or racism. The glass ceiling refers thus to vertical discrimination, most frequently against women in companies. Feminists first used the metaphor about barriers in the careers of high-achieving women. Marilyn Loden coined it during a 1978 speech. In the US, the concept is sometimes extended to refer to obstacles hindering the advancement of minority women, as well as minority men.
Gendered citation patterns among the scientific elite
It's 2022, and women in science are still less likely than their male peers to be hired and promoted. Women are less likely to be mentored by eminent faculty, they publish in less prestigious journals, have fewer collaborators, are underrepresented among journal reviewers and editors, and their papers receive fewer citations.
USC's Information Sciences Institute (ISI) Principal Scientist Kristina Lerman and her team used AI to find answers to this question. The resulting paper was published in the prestigious, peer-reviewed, multi-disciplinary science journal Proceedings of the National Academy of Sciences (PNAS) on September 26, 2022.
As a woman in science, Lerman knows the world she works in. Still, she was shocked by statistics she recently learned: only two percent of Nobel Prize winners in physics have been women (until a few years ago, that was one percent), and those numbers are similar across many scientific fields.
Lerman said, "only seven percent of Nobel Prize winners in chemistry have been women! Women have worked in chemistry for a long time, so how is that? We were curious about this discrepancy."
Lerman had the right dataset for the problem. Since 2019, she and her team have been working on a large project that used AI to predict the reproducibility of research papers. Funded by DARPA (the Defense Advanced Research Projects Agency), the ISI team used AI to analyze many aspects of scientific papers, including citations, to predict reproducibility. They published the paper "Assessing Scientific Research Papers with Knowledge Graphs" at ACM SIGIR 22 (the Association for Computing Machinery's Special Interest Group on Information Retrieval) in July 2022, describing their novel method and promising findings.
To do this reproducibility research, Lerman's team gathered a huge amount of data on academic papers.
Her co-author Jay Pujara, director of the Center on Knowledge Graphs at ISI, said, "We collected this huge citation graph - the network of papers, authors, citations, references, collaborations, author institutions, where they publish, etc."
They turned this data into a vast knowledge graph (a "knowledge graph" is a representation of a network of real-world entities that illustrates the relationships between them).
The team looked at the shapes or "structures" that arose in the knowledge graph. They wondered if some natural phenomenon was causing the different structures in the citation networks. Additionally, they wanted to make sure that biases were not impacting the data used in their reproducibility predictions in the data.
Pujara said, "Kristina [Lerman] had the idea to look at covariates like gender or prestige."
And with that idea, the team of researchers set out to see if there was a difference in a network based on whether the author was a man or a woman, as well as if they were at a top-ranked university or a lower-ranked university.
Before we go any further, a little info on how citation in scientific research works. There are typically three reasons an author might cite another author's paper.
"Trying to study the citation network for every researcher out there is hard, so why don't we pick the cream of the crop?" said Pujara.
The team looked at scientists elected to the US National Academy of Sciences (NAS), one of the oldest and most prominent professional science organizations. New members of NAS are elected by current members based on a distinguished record of scientific achievement, meaning, in theory, they've all reached the same echelon of recognition. The ISI team looked at 766 NAS researchers, 120 of whom were women, hypothesizing that complex gender differences would be visible within this group of elite scientists.
Their hypothesis proved correct.
They constructed citation networks that captured each NAS member's structure of peer recognition. These structures differed significantly between male and female NAS members. Women's networks were much more tightly clustered, indicating that a female scientist must be more socially embedded and have a stronger support network than her male counterparts. The differences were systemic enough to allow the gender of the member to be accurately classified based on their citation network alone.
Lerman said:
"We could write an AI algorithm that would look at the citation networks and predict whether this was the citation network of a woman or a man. This was pretty shocking and disappointing to us."
As a controlled study, the team also looked at the covariate of prestige. Like women, NAS members affiliated with less prestigious institutions are a minority. Lerman said, "we would have imagined that maybe women's citation networks would look like those of members from non-prestigious universities." But that was not the case. They did not observe any disparities due to the prestige of a member's institutional affiliation.
Conclusion:
Based on a scientist's citation network alone, gender can accurately be determined, but the prestige of the university that the scientist is affiliated with cannot. This suggests that gender continues to influence career success in science, according to the ISI team.
Why is this happening? Pujara said:
"We don't know. It could be because some aspect of gender changes collaborative behavior. Or it might be something about society that shapes researchers and their paths based on social biases. So we don't know the answer to that. What we know is that there's a difference."
The real question is: how can we change it? How can we make science a less hostile climate for women, remove the barriers to opportunities for women, and create an environment that allows women to rise to the top of their fields?
The ISI team hopes their methods and results can help move forward. To start, this study could help researchers understand their networks. Additionally, it could be used as a way for policymakers to understand if programs aiming to improve gender equity in science are working.
Finally, and importantly, we can learn from those differences in the citation structures between men and women.
"For a woman to be recognized, she has to be well-embedded and have a strong support network," Lerman said. "Mentoring young women and telling them they have to build those networks of social support, and be very intentional about them" seems to be one way to change the shape of these structures and science.
Gender Disparities in Science: Breaking the Glass Ceiling by Looking at Citations | University of Southern California (usc.edu). SexualDiversity.org makes no warranties or representations in connection therewith. Content may have been edited for style, clarity or length.
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• (APA): University of Southern California. (2022, October 5). Gender Disparities in Science: Breaking the Glass Ceiling by Looking at Citations. SexualDiversity.org. Retrieved October 4, 2024 from www.sexualdiversity.org/discrimination/equality/1034.php
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