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

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 aspe...

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Detalles Bibliográficos
Autores principales: Steinfeldt, Christian, Mihaljević, Helena
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9889827/
https://www.ncbi.nlm.nih.gov/pubmed/36743404
http://dx.doi.org/10.3389/fdata.2022.989469
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author Steinfeldt, Christian
Mihaljević, Helena
author_facet Steinfeldt, Christian
Mihaljević, Helena
author_sort Steinfeldt, Christian
collection PubMed
description 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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spelling pubmed-98898272023-02-02 A machine learning approach to quantify gender bias in collaboration practices of mathematicians Steinfeldt, Christian Mihaljević, Helena Front Big Data Big Data 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. Frontiers Media S.A. 2023-01-18 /pmc/articles/PMC9889827/ /pubmed/36743404 http://dx.doi.org/10.3389/fdata.2022.989469 Text en Copyright © 2023 Steinfeldt and Mihaljević. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Big Data
Steinfeldt, Christian
Mihaljević, Helena
A machine learning approach to quantify gender bias in collaboration practices of mathematicians
title A machine learning approach to quantify gender bias in collaboration practices of mathematicians
title_full A machine learning approach to quantify gender bias in collaboration practices of mathematicians
title_fullStr A machine learning approach to quantify gender bias in collaboration practices of mathematicians
title_full_unstemmed A machine learning approach to quantify gender bias in collaboration practices of mathematicians
title_short A machine learning approach to quantify gender bias in collaboration practices of mathematicians
title_sort machine learning approach to quantify gender bias in collaboration practices of mathematicians
topic Big Data
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9889827/
https://www.ncbi.nlm.nih.gov/pubmed/36743404
http://dx.doi.org/10.3389/fdata.2022.989469
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