Increasing evidence of women’s under-representation in some scientific disciplines is prompting researchers to expand our understanding of this social phenomenon. Moreover, any countermeasures proposed to eliminate this under-representation should be tailored to the actual reasons for this different participation. Here, we take a multi-dimensional approach to assessing gender differences in science by representing scientific communities as social networks, and using data analytics, complexity science methods, and semantic methods to measure gender differences in the context, the attitude and the success of scientists. We apply this approach to four scientific communities in the two fields of computer science and information systems using the network of authors at four different conferences. For each discipline, one conference is based in Italy and attracts mostly Italians, while one conference is international in both location and participants. The present paper provides evidence against common narratives that women’s under-representation is due to women’s limited skills and/or less social centrality.

在某些科学学科中，妇女任职人数不足的证据越来越多，这促使研究人员加深了对这一社会现象的理解。此外，为消除这种代表性不足而提出的任何对策都应针对这种不同参与的实际原因进行调整。在这里，我们采用多维方法来评估科学中的性别差异，方法是将科学社区表示为社交网络，并使用数据分析，复杂性科学方法和语义方法来衡量背景，科学家的态度和成功中的性别差异。 。我们使用作者网络在四个不同的会议上将这种方法应用于计算机科学和信息系统两个领域中的四个科学界。对于每个学科，一个会议位于意大利，主要吸引意大利人，而一个会议在地点和参与者方面都是国际性的。本文提供了反对常见说法的证据，即妇女任职人数不足是由于妇女的技能有限和/或较少的社会中心地位所致。