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简历语言中的性别差异与薪资期望中的性别差距。

Gender differences in resume language and gender gaps in salary expectations.

作者信息

Qu Qian, Liu Quan-Hui, Gao Jian, Huang Shudong, Feng Wentao, Yue Zhongtao, Lu Xin, Zhou Tao, Lv Jiancheng

机构信息

College of Computer Science, Sichuan University, Chengdu, People's Republic of China.

College of Information and Communication, National University of Defense Technology, Wuhan, People's Republic of China.

出版信息

J R Soc Interface. 2025 Jun;22(227):20240784. doi: 10.1098/rsif.2024.0784. Epub 2025 Jun 4.

Abstract

How men and women present themselves in their resumes may affect their opportunity in job seeking. To investigate gender differences in resume writing and how they are associated with gender gaps in the labour market, we analysed 6.9 million resumes of Chinese job applicants in this study. Results reveal substantial gender resume differences, where women and men show distinct patterns in both simple language features and high-level semantic structures in the word embedding space of resumes. In particular, women tend to use shorter resumes, longer sentences and a more diverse set of unique words. Neural network models trained on resumes can predict gender with 80% accuracy, and the accuracy decreases with education levels and text standardization requirements. Moreover, while better language skills are associated with higher salary expectations, this positive relationship is magnified for men but weakened for women in women-dominated occupations. This study presents a new venue for the understanding of gender differences and provides empirical findings on how men and women are different in self-portraying and job seeking.

摘要

男性和女性在简历中展现自己的方式可能会影响他们求职的机会。为了研究简历撰写中的性别差异以及这些差异如何与劳动力市场中的性别差距相关联,我们在本研究中分析了690万份中国求职者的简历。结果显示出简历存在显著的性别差异,男性和女性在简历的词嵌入空间中的简单语言特征和高级语义结构方面都呈现出不同的模式。特别是,女性倾向于使用更短的简历、更长的句子以及更多样化的独特词汇。基于简历训练的神经网络模型能够以80%的准确率预测性别,并且准确率会随着教育水平和文本标准化要求而降低。此外,虽然更好的语言技能与更高的薪资期望相关,但在女性主导的职业中,这种正相关关系对男性来说被放大,而对女性来说则被削弱。本研究为理解性别差异提供了一个新的视角,并提供了关于男性和女性在自我展示和求职方面如何存在差异的实证研究结果。

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