Cross-sectional analysis of self-promotional language in texts of grant proposals across gender

Abstract To investigate whether the use of positive, self-promotional language in grant proposals is associated with the gender of the applicant, we used a cross-sectional analysis on 8,150 grant proposals submitted to the Swiss National Science Foundation in life sciences disciplines across three different funding schemes, targeting applicants at different career stages. Multivariate, logistic, and negative binomial regressions were used to model the count and binary presence of positive words in grant proposal titles and abstracts. We found no evidence of an association between the use of self-promotional language and gender in grant proposals from life sciences disciplines. In the Projects funding scheme, funding bottom-up research, male applicants have used on average slightly more positive words in their proposals than their female peers, but the estimates (Rate Ratio 1.057; 95% CI 0.982–1.139) show no evidence of an effect. Similarly, no evidence of gender differences was found in proposals from the Careers funding schemes (Rate Ratio 1.087, 95% CI 0.973–1.214) which target early career researchers, and in a pilot scheme (Rate Ratio 1.089, 95% CI 0.911–1.302), a double-blind funding scheme supporting unconventional research. It is encouraging that we found no evidence indicating a difference in the use of positive words between male and female grant applicants. Although many factors influence how peer reviewers perceive the content of grant proposals, language usage undoubtedly plays an important role. We urge other funding agencies to conduct similar studies to identify if language usage contributes to biases in grant evaluation.

Authors

Institutions

Publication Details

Journal
Research Evaluation
Published
2024-12-27
DOI
https://doi.org/10.1093/reseval/rvaf002
Primary Topic
Gender Diversity and Inequality
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Cross-sectional analysis of self-promotional language in texts of grant proposals across gender

Sandar Felicity Lim, Rachel Heyard, Anne Jorstad
Research Evaluation
Gender Diversity and Inequality
article

Cross-sectional analysis of self-promotional language in texts of grant proposals across gender

Sandar Felicity Lim, Rachel Heyard, Anne Jorstad
article en

Abstract

Abstract To investigate whether the use of positive, self-promotional language in grant proposals is associated with the gender of the applicant, we used a cross-sectional analysis on 8,150 grant proposals submitted to the Swiss National Science Foundation in life sciences disciplines across three different funding schemes, targeting applicants at different career stages. Multivariate, logistic, and negative binomial regressions were used to model the count and binary presence of positive words in grant proposal titles and abstracts. We found no evidence of an association between the use of self-promotional language and gender in grant proposals from life sciences disciplines. In the Projects funding scheme, funding bottom-up research, male applicants have used on average slightly more positive words in their proposals than their female peers, but the estimates (Rate Ratio 1.057; 95% CI 0.982–1.139) show no evidence of an effect. Similarly, no evidence of gender differences was found in proposals from the Careers funding schemes (Rate Ratio 1.087, 95% CI 0.973–1.214) which target early career researchers, and in a pilot scheme (Rate Ratio 1.089, 95% CI 0.911–1.302), a double-blind funding scheme supporting unconventional research. It is encouraging that we found no evidence indicating a difference in the use of positive words between male and female grant applicants. Although many factors influence how peer reviewers perceive the content of grant proposals, language usage undoubtedly plays an important role. We urge other funding agencies to conduct similar studies to identify if language usage contributes to biases in grant evaluation.

Research EvaluationVol. 34
University of Zurich (CH), Prevention Institute (US), Swiss National Science Foundation (CH)
Openalex Percentile: Top 62%
Gender Diversity and Inequality
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.