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 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.

[1]  Eitan Frachtenberg,et al.  Gender Differences in Collaboration Patterns in Computer Science , 2022, Publ..

[2]  M. Kwiek,et al.  Are female scientists less inclined to publish alone? The gender solo research gap , 2021, Scientometrics.

[3]  S. Goyal,et al.  Gender and Collaboration , 2021, Review of Economics and Statistics.

[4]  Ludo Waltman,et al.  Gender differences in scientific careers: A large-scale bibliometric analysis , 2021, ArXiv.

[5]  Antonio De Nicola,et al.  Assessment of gender divide in scientific communities , 2021, Scientometrics.

[6]  Arthur Schram,et al.  Gender Differences in Recognition for Group Work , 2015 .

[7]  Maxime B. Sainte-Marie,et al.  Who are the acknowledgees? An analysis of gender and academic status , 2020, Quantitative Science Studies.

[8]  Brian K. Ryu The Demise of Single-Authored Publications in Computer Science: A Citation Network Analysis , 2020, ArXiv.

[9]  Helena Mihaljević,et al.  Reflections on Gender Analyses of Bibliographic Corpora , 2019, Front. Big Data.

[10]  David G. Pina,et al.  Effects of seniority, gender and geography on the bibliometric output and collaboration networks of European Research Council (ERC) grant recipients , 2019, PloS one.

[11]  Agnieszka Olechnicka,et al.  The Geography of Scientific Collaboration , 2018 .

[12]  Lucía Santamaría,et al.  Comparison and benchmark of name-to-gender inference services , 2018, PeerJ Comput. Sci..

[13]  Mohsen Jadidi,et al.  Gender Disparities in Science? Dropout, Productivity, Collaborations and Success of Male and Female Computer Scientists , 2017, Adv. Complex Syst..

[14]  Michael Bode,et al.  On the extinction of the single-authored paper : the causes and consequences of increasingly collaborative applied ecological research. , 2018 .

[15]  S. Goyal,et al.  Gender & Collaboration , 2018 .

[16]  Silvio Lattanzi,et al.  Ego-Splitting Framework: from Non-Overlapping to Overlapping Clusters , 2017, KDD.

[17]  Scott Lundberg,et al.  A Unified Approach to Interpreting Model Predictions , 2017, NIPS.

[18]  Christoph Müller,et al.  Data sets for author name disambiguation: an empirical analysis and a new resource , 2017, Scientometrics.

[19]  John O'Hagan,et al.  Rise of multi-authored papers in economics: Demise of the ‘lone star’ and why? , 2017, Scientometrics.

[20]  Neven Caplar,et al.  Quantitative evaluation of gender bias in astronomical publications from citation counts , 2016, Nature Astronomy.

[21]  Filippo Radicchi,et al.  Differences in Collaboration Patterns across Discipline, Career Stage, and Gender , 2016, PLoS biology.

[22]  Marco Tullney,et al.  The Effect of Gender in the Publication Patterns in Mathematics , 2016, PloS one.

[23]  A. Allen BY HARDY AND LITTLEWOOD , 2016 .

[24]  Lorenzo Ductor Does Co‐Authorship Lead to Higher Academic Productivity? , 2015 .

[25]  Cecilia Mascolo,et al.  The Evolution of Your Success Lies at the Centre of Your Co-Authorship Network , 2015, PloS one.

[26]  W. Vélez,et al.  Fall 2013 Departmental Profile Report , 2016 .

[27]  Ilse Lindenlaub,et al.  Gender, Social Networks And Performance , 2014 .

[28]  Marjorie M. K. Hlava,et al.  Publishing: Credit where credit is due , 2014, Nature.

[29]  Ingo Scholtes,et al.  Predicting scientific success based on coauthorship networks , 2014, EPJ Data Science.

[30]  Fabian Müller,et al.  Author Profile Pages in zbMATH - Improving Accuracy through User Interaction , 2014, CICM Workshops.

[31]  Carl T. Bergstrom,et al.  The Role of Gender in Scholarly Authorship , 2012, PloS one.

[32]  Monica Gaughan,et al.  How do men and women differ in research collaborations? An analysis of the collaborative motives and strategies of academic researchers☆ , 2011 .

[33]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[34]  N. Vafeas,et al.  Determinants of single authorship , 2010 .

[35]  Benjamin F. Jones,et al.  Supporting Online Material Materials and Methods Figs. S1 to S3 References the Increasing Dominance of Teams in Production of Knowledge , 2022 .

[36]  Cecilia,et al.  Are Emily and Greg More Employable Than Lakisha and Jamal ? A Field Experiment on Labor Market Discrimination , 2007 .

[37]  Pushkar Maitra SCHOOL OF ECONOMICS AND FINANCE , 2006 .

[38]  Miriam Farber,et al.  Single-authored publications in the sciences at Israeli universities , 2005, J. Inf. Sci..

[39]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[40]  Paul J. Campbell,et al.  Patterns of Collaboration in Mathematical Research (Book) , 2003 .

[41]  Robin J. Wilson Cambridge Scientific Minds: Hardy and Littlewood , 2002 .

[42]  J. Friedman Greedy function approximation: A gradient boosting machine. , 2001 .

[43]  D. Price Little Science, Big Science , 1965 .