TY - JOUR A1 - Steinfeldt, Christian A1 - Mihaljević, Helena T1 - A machine learning approach to quantify gender bias in collaboration practices of mathematicians JF - Frontiers in Big Data N2 - 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. KW - Maschinelles Lernen KW - collaboration networks KW - machine learning KW - gender in mathematics KW - regression-based analysis KW - authorship KW - scientific publishing KW - single-authored publications KW - coauthorship KW - Geschlechterrolle KW - Mathematik KW - Regressionsanalyse KW - Kollaboratives Schreiben KW - Autorschaft Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:523-16674 SN - 2624-909X VL - 5 SP - 1 EP - 17 PB - Frontiers Media S.A. ER -