Promoting Gender-Fair Language: The Impact of Arguments on Language Use, Attitudes, and Cognitions
Abstract
The present research investigates whether arguments encourage speakers to use and to approve of gender-fair language. We collected and pretested arguments regarding gender-fair language and masculine generics and created four messages which supported either gender-fair usage or masculine generics (strong and weak arguments) as well as two control texts. Results showed that speakers changed their language behavior more in the direction of gender-fairness when they had been exposed to arguments for gender-fair language than after control texts. We did not find any effect of arguments promoting masculine generics and no effect on cognitive responses and attitudes. Taken together, these results show that arguments promoting gender-fair language can motivate speakers to use gender-fair wording, a new and important finding in the context of implementing gender equality in language.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In a study where participants were exposed to arguments promoting gender-inclusive language (Koeser and Sczesny, 2014), both strong and weaker arguments increased participants' use of gender-inclusive language in a following cloze task.
“Both strong arguments (e.g., gender-inclusive language as a crucial factor for gender equality) and weaker ones (e.g., existing evidence is ideology-free and therefore reliable) increased participants’ use of gender-inclusive language in a following cloze task.”
From What affects people’s use of and attitudes toward gender-inclusive language? A literature review (2025) · cites this paper as Koeser and Sczesny, 2014
Koeser and Sczesny (2014) attempted to influence German-speaking participants toward more positive attitudes regarding gender-inclusive language by letting them read arguments supporting inclusive language, but without success.
“Koeser and Sczesny (2014) attempted to influence German-speaking participants toward more positive attitudes regarding gender-inclusive language through letting them read arguments supporting inclusive language, but without success.”
From What affects people’s use of and attitudes toward gender-inclusive language? A literature review (2025) · cites this paper as Koeser and Sczesny (2014)
The effect of arguments on gender-inclusive language use was more pronounced among women than men.
“This effect was again more pronounced among women than men.”
From What affects people’s use of and attitudes toward gender-inclusive language? A literature review (2025) · cites this paper as Koeser and Sczesny, 2014
Cited by
Sorted by citations
What affects people’s use of and attitudes toward gender-inclusive language? A literature review
Lange, E., & von Stockhausen, L. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1657753 · Cited by 5 · see citing works
- Authors
- Koeser, S., & Sczesny, S.
- Affiliations
- University of Bern (CH)
- ORCID
- Sczesny, Sabine
- Venue
- Journal of Language and Social Psychology
- Publisher
- SAGE Publications
- Published
- 2014-06-27
- Volume
- 33
- Issue
- 5
- Pages
- 548-560
- ISSN
- 0261-927X (ISSN-L)
- 1552-6526
- OpenAlex
- W2159122500
- Cited by
- 52
- References
- 0
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 6 · 2023: 8 · 2022: 4 · 2021: 3 · 2020: 3 · 2019: 2 · 2018: 7 · 2017: 2 · 2016: 3 · 2015: 4 · 2014: 1 · 2013: 1
- FWCI
- 8.49
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Language and Social Psychology (publishedVersion)
- Topics
- Social Sciences › Gender Studies › Gender Studies in Language
- Social Sciences › Linguistics and Language › Linguistic Variation and Morphology
- Social Sciences › Sociology and Political Science › Social and Intergroup Psychology
- Keywords
- gender-inclusive language, generic masculine, language attitudes, gender equality in language, persuasive messages, cognitive responses
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
