Gender Differences in Collaboration Patterns in Computer Science

Yamamoto, Josh; Frachtenberg, Eitan

doi:10.3390/publications10010010

Cited by 5 publications

(7 citation statements)

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“…For the remaining 3,331 authors, we looked up genders manually on the web as we have with systems conferences, leaving only 162 people for which we could not assign a gender manually or automatically. The overall gender statistics for these conferences are shown in Table 2 , and the full details on this auxiliary dataset are available in the original study of that data [ 31 ].…”

Section: Methodsmentioning

confidence: 99%

Underrepresentation of women in computer systems research

Frachtenberg

Kaner

2022

PLoS ONE

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The gender gap in computer science (CS) research is a well-studied problem, with an estimated ratio of 15%–30% women researchers. However, far less is known about gender representation in specific fields within CS. Here, we investigate the gender gap in one large field, computer systems. To this end, we collected data from 72 leading peer-reviewed CS conferences, totalling 6,949 accepted papers and 19,829 unique authors (2,946 women, 16,307 men, the rest unknown). We combined these data with external demographic and bibliometric data to evaluate the ratio of women authors and the factors that might affect this ratio. Our main findings are that women represent only about 10% of systems researchers, and that this ratio is not associated with various conference factors such as size, prestige, double-blind reviewing, and inclusivity policies. Author research experience also does not significantly affect this ratio, although author country and work sector do. The 10% ratio of women authors is significantly lower than the 16% in the rest of CS. Our findings suggest that focusing on inclusivity policies alone cannot address this large gap. Increasing women’s participation in systems research will require addressing the systemic causes of their exclusion, which are even more pronounced in systems than in the rest of CS.

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Section: Methodsmentioning

confidence: 99%

Underrepresentation of women in computer systems research

Frachtenberg

Kaner

2022

PLoS ONE

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“…In particular, men develop significantly larger networks, even when controlling for time variables such as the year or seniority, though with small effect size. In a recent descriptive analysis of major conferences in computer systems in 2017 (Yamamoto and Frachtenberg, 2022 ), men were shown to have more coauthors per paper and overall than women, but the difference was rather small. An analysis of four Italian conferences in the fields of information systems and computer science revealed that “men are more key than women” in only one of the four considered communities.…”

Section: Related Workmentioning

confidence: 99%

“…As shown by Sarsons et al ( 2021 ), female and male economists who write most of their papers alone have similar tenure rates, conditional on the quality of their contributions. Existing studies on sole authorship seem to agree that women write a smaller proportion of their publications alone (West et al, 2013 ; Ductor et al, 2018 ; Sarsons et al, 2021 ; Yamamoto and Frachtenberg, 2022 ). In one of the few works addressing the question of gender and solo research in more detail, however, Kwiek and Roszka ( 2022 ) find only marginal gender differences among researchers at Polish universities.…”

Section: Related Workmentioning

confidence: 99%

A machine learning approach to quantify gender bias in collaboration practices of mathematicians

Steinfeldt

Mihaljević

2023

Front. Big Data

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Collaboration practices have been shown to be crucial determinants of scientific careers. We examine the effect of gender on coauthorship-based collaboration in mathematics, a discipline in which women continue to be underrepresented, especially in higher academic positions. We focus on two key aspects of scientific collaboration—the number of different coauthors and the number of single authorships. A higher number of coauthors has a positive effect on, e.g., the number of citations and productivity, while single authorships, for example, serve as evidence of scientific maturity and help to send a clear signal of one's proficiency to the community. Using machine learning-based methods, we show that collaboration networks of female mathematicians are slightly larger than those of their male colleagues when potential confounders such as seniority or total number of publications are controlled, while they author significantly fewer papers on their own. This confirms previous descriptive explorations and provides more precise models for the role of gender in collaboration in mathematics.

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“…The complete methodological aspects of identifying and cleaning the appropriate data as well as the main statistical findings about the underrepresentation of women in the field can be found in our original study. 1 Here, we combine our current dataset with prior studies to try to evaluate the impact of each of the 10 potential factors on the increased gender gap in systems, each presented as a hypothesis that it has a more significant effect on the gender gap in systems in particular. The 10 hypotheses are organized into two groups: six where our data provide evidence for or against an increased effect and four for which we have no specific data but can speculate based on the characteristics of systems research.…”

Section: Notes From the Fieldmentioning

confidence: 99%

“…

In a recent study, we found that the ratio of femaleto-male authors in computer systems conferences is particularly low, even compared to the rest of computer science (CS). 1 The large and statistically significant underrepresentation cannot be fully explained by review bias, differences in collaboration patterns, and numerous other demographic and conference factors alone. 2

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confidence: 99%