Evidence map›Paper›PMID 40462711›Full record

ArticleJournal of the Royal Society, Interface2025

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

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

Abstract read
In one paragraph

Article in Journal of the Royal Society, Interface, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Qian QuCollege of Computer Science, Sichuan University, Chengdu, People's Republic of China.
Quan-Hui LiuCollege of Computer Science, Sichuan University, Chengdu, People's Republic of China.ORCID 0000-0002-9481-7964
Jian GaoFaculty of Social Sciences, The University of Hong Kong, Hong Kong SAR, People's Republic of China.ORCID 0000-0001-6659-5770
Shudong HuangCollege of Computer Science, Sichuan University, Chengdu, People's Republic of China.
Wentao FengCollege of Computer Science, Sichuan University, Chengdu, People's Republic of China.
Zhongtao YueCompleX Lab, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.
Xin LuCollege of Systems Engineering, National University of Defense Technology, Changsha, People's Republic of China.ORCID 0000-0002-3547-6493
Tao ZhouCompleX Lab, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.ORCID 0000-0003-0561-2316
Jiancheng LvCollege of Computer Science, Sichuan University, Chengdu, People's Republic of China.

Funding

Higher Education Discipline Innovation ProjectNational Natural Science Foundation of ChinaScience Fund for Creative Research Groups of Sichuan Province Natural Science Foundation
6 · The paper itself

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.

Indexed as

EmploymentLanguageSalaries and Fringe BenefitsAdultChinaFemaleHumansMaleSex Factorscomputational social sciencegender gapneural network modelresumewritten language

Identifiers

PMID40462711
PMCPMC12134937

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.