Gendered cultural discourse in Japan across the prewar–postwar transition: evidence from historical word embeddings

Abstract We quantify the evolution of gender stereotypes in Japan from 1900 to 1998, covering the prewar–postwar transition, using a series of yearly word embeddings trained on historical text corpora. We define the gender stereotype value to measure the strength of a word’s gender association by computing the difference in cosine similarity of the word to female- versus male-related attribute words. We examine trajectories of gender stereotype across three traditionally gendered domains: Home, Work, and Politics. To provide a more granular analysis and strengthen the robustness of our findings in the Work domain, we also examine changes in gender stereotypes across 18 occupations and calculate their correlations with gender participation statistics. Our results reveal domain-specific patterns. In the Home domain, female stereotype values remain stable over time, showing no statistically significant changes. In contrast, the Work and Politics domains exhibit significant trend reversals in female stereotype values around 1945, shifting from negative prewar trends to positive postwar trends, indicating increasing female associations in public domains over time. These findings show that trends in gendered cultural discourse shifted unevenly around 1945: the observed changes in the Work and Politics domains are temporally aligned with postwar institutional transformations, including reforms under the U.S.-led Allied Occupation, while the Home domain shows more limited change. Furthermore, female stereotype values for occupations show a moderately positive correlation (r=0.364) with the proportion of women in each occupation, indicating that word-embedding-based measures of gender stereotype mirrored demographic shifts to a meaningful extent.

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Publication Details

Journal
EPJ Data Science
Published
2026-08-18
DOI
https://doi.org/10.1140/epjds/s13688-026-00688-5
Primary Topic
Computational and Text Analysis Methods
Type
article
Field-Weighted Citation Impact
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article

Gendered cultural discourse in Japan across the prewar–postwar transition: evidence from historical word embeddings

Haewoon Kwak, Akira Matsui, Jisun An
EPJ Data Science
Computational and Text Analysis Methods
article

Gendered cultural discourse in Japan across the prewar–postwar transition: evidence from historical word embeddings

Haewoon Kwak, Akira Matsui, Jisun An
article en

Abstract

Abstract We quantify the evolution of gender stereotypes in Japan from 1900 to 1998, covering the prewar–postwar transition, using a series of yearly word embeddings trained on historical text corpora. We define the gender stereotype value to measure the strength of a word’s gender association by computing the difference in cosine similarity of the word to female- versus male-related attribute words. We examine trajectories of gender stereotype across three traditionally gendered domains: Home, Work, and Politics. To provide a more granular analysis and strengthen the robustness of our findings in the Work domain, we also examine changes in gender stereotypes across 18 occupations and calculate their correlations with gender participation statistics. Our results reveal domain-specific patterns. In the Home domain, female stereotype values remain stable over time, showing no statistically significant changes. In contrast, the Work and Politics domains exhibit significant trend reversals in female stereotype values around 1945, shifting from negative prewar trends to positive postwar trends, indicating increasing female associations in public domains over time. These findings show that trends in gendered cultural discourse shifted unevenly around 1945: the observed changes in the Work and Politics domains are temporally aligned with postwar institutional transformations, including reforms under the U.S.-led Allied Occupation, while the Home domain shows more limited change. Furthermore, female stereotype values for occupations show a moderately positive correlation (r=0.364) with the proportion of women in each occupation, indicating that word-embedding-based measures of gender stereotype mirrored demographic shifts to a meaningful extent.

EPJ Data Science
Indiana University Bloomington (US), Kobe University (JP)
Openalex Percentile: Top 99%
Computational and Text Analysis Methods
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