Steinfeldt Christian, Mihaljević Helena
Department 4 - Computer Science, Communication and Economics, Hochschule für Technik und Wirtschaft Berlin, University of Applied Sciences, Berlin, Germany.
Front Big Data. 2023 Jan 18;5:989469. doi: 10.3389/fdata.2022.989469. eCollection 2022.
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.
合作实践已被证明是科学职业的关键决定因素。我们研究了性别对数学领域基于共同作者的合作的影响，在这一学科中，女性的代表性仍然不足，尤其是在高等学术职位上。我们关注科学合作的两个关键方面——不同共同作者的数量和独著的数量。更多的共同作者对例如被引用次数和产出率有积极影响，而独著，例如，可作为科学成熟度的证据，并有助于向学术界发出一个人能力的明确信号。使用基于机器学习的方法，我们表明，在控制了资历或出版物总数等潜在混杂因素后，女数学家的合作网络比男同事的略大，而她们独立撰写的论文明显较少。这证实了之前的描述性探索，并为性别在数学合作中的作用提供了更精确的模型。
