The results show that reading a text in gender-fair language before processing the riddle led to higher rates of answers indicating that the specialist is a women compared to reading a text in masculine generics (44% vs. Subsequently, participants were asked to solve the specialist riddle. We randomly assigned 517 native German speakers (68% women) to reading a text on expertise written either in gender-fair language or in masculine generics. We expected that reading a text in gender-fair language before processing the specialist riddle helps readers achieve control over automatically activated gender stereotypes and thus facilitates the restructuring and reinterpretation of the problem, which is necessary to reach the conclusion that the specialist is a woman. The difficulty of this thinking task lies in the fact that a gender-unspecified specialist is often automatically assumed to be a man due to gender stereotypes. We used answers to the specialist riddle as an indicator for male bias in mental representations of expertise. This experimental online-survey study investigated if different written language forms in German have an effect on male bias in thinking. While the existing literature on gendered language has been an important and useful tool for reading a text in the context of digital humanities, an innovative textual analytics approach, as shown in this paper, can prove to be a crucial indicator of historical trends and variations in social development. These findings are discussed with consideration of historical, cultural, social, psychological, and geographical factors. This quantitative study shows that (1) the use of gendered language has weakened over time, indicating a decline in occupational gender stereotyping (2) conservative genres have shown higher degrees of gendered language use (3) culturally conservative, demographically stable, or geographically remote regions have higher degrees of gendered language use. The index was then applied to explore the trends and variations of gender equality in occupation, drawing on an extensive digital collection of materials published by the largest newspaper group in China for both longitudinal (from 1946 to 2018) and synchronic (from 31 provincial-level administrative divisions) data.
The effectiveness of the linguistic index in modelling occupational gender segregation was confirmed through survey responses from 244 participants, covering 63 occupations listed in the Holland Occupational Codes.
Specifically, we have designed a linguistic index that measures the marked usage of gendered modifiers in the Chinese language this predicts the degree of occupational gender segregation by identifying the unbalanced distribution of males and females across occupations. This paper proposes a textual analytics approach to the discovery of trends and variations in social development.
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