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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.02425 |
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| _version_ | 1866916774965936128 |
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| author | Liu, Tairan |
| author_facet | Liu, Tairan |
| contents | Textbooks play a critical role in shaping children's understanding of the world. While previous studies have identified gender inequality in individual countries' textbooks, few have examined the issue cross-culturally. This study applies natural language processing methods to quantify gender inequality in English textbooks from 22 countries across 7 cultural spheres. Metrics include character count, firstness (which gender is mentioned first), and TF-IDF word associations by gender. The analysis also identifies gender patterns in proper names appearing in TF-IDF word lists, tests whether large language models can distinguish between gendered word lists, and uses GloVe embeddings to examine how closely keywords associate with each gender. Results show consistent overrepresentation of male characters in terms of count, firstness, and named entities. All regions exhibit gender inequality, with the Latin cultural sphere showing the least disparity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_02425 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Gender Inequality in English Textbooks Around the World: an NLP Approach Liu, Tairan Computation and Language Applications Textbooks play a critical role in shaping children's understanding of the world. While previous studies have identified gender inequality in individual countries' textbooks, few have examined the issue cross-culturally. This study applies natural language processing methods to quantify gender inequality in English textbooks from 22 countries across 7 cultural spheres. Metrics include character count, firstness (which gender is mentioned first), and TF-IDF word associations by gender. The analysis also identifies gender patterns in proper names appearing in TF-IDF word lists, tests whether large language models can distinguish between gendered word lists, and uses GloVe embeddings to examine how closely keywords associate with each gender. Results show consistent overrepresentation of male characters in terms of count, firstness, and named entities. All regions exhibit gender inequality, with the Latin cultural sphere showing the least disparity. |
| title | Gender Inequality in English Textbooks Around the World: an NLP Approach |
| topic | Computation and Language Applications |
| url | https://arxiv.org/abs/2506.02425 |