Reducing a Male Bias in Language? Establishing the Efficiency of Three Different Gender-Fair Language Strategies
By A. Lindqvist, Emma A. Renström, Marie Gustafsson Sendén. Sex Roles, 2018
113 citations recorded by the source corpus.
Different strategies of gender-fair language have been applied to reduce a male bias, which means the implicit belief that a word describing an undefined person describes a man. This male bias might be caused by the words themselves in terms of generic masculine or masculine forms or by androcentrism (the conflation of men with humanity). In two experiments, we tested how different gender-fair strategies used as labels of an unknown social target (an applicant in a recruitment situation) could eliminate the male bias. The three types of gender-fair strategies tested were: (a) paired forms (he/she), (b) traditional neutral words (e.g., singular they, “the applicant”), or (c) gender-neutral third-person pronouns actively created to challenge the binary gender system (ze, Swedish hen). The two experiments were performed in Swedish with 417 undergraduates in Sweden and in English with 411 U.S. participants recruited online. In Swedish, the third-person gender-neutral pronoun singular (hen) was used. In English, several forms of such gender-neutral pronouns have been suggested (e.g., ze). In both experiments, results indicated that paired forms and actively created gender-neutral pronouns eliminated the male bias, whereas traditional neutral words contained a male bias. Thus, gender-fair language strategies should avoid using traditional words. Consequences of using paired forms and creating new gender-neutral words are discussed. We argue that an actively created gender-neutral pronoun is of highest value because it is more inclusive.
Subfields: Gender Studies, Genetics
View Paper: https://www.semanticscholar.org/paper/736f2e8b1ebc7e667e00883d520296151454b969
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Gender Studies
Citations in the corpus
- Defending the Sex/Gender Binary: The Role of Gender Identification and Need for Closure (2020, background): “Findings from this literature show that language has powerful effects both on who comes to mind when using differently gendered terms (Lindqvist et al., 2019) and also how individuals described in gender-fair language are perceived (Budziszewska et al.”
- Gender Bias in Machine Translation (2021, background): “In a community increasingly conscious of the power of language to hardwire stereotypical beliefs and women’s invisibility (Lindqvist et al., 2019; Beukeboom and Burgers, 2019), the bias exhibited by the system was confronted with a new sensitivity.”