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.

翻译：教科书在塑造儿童对世界的认知方面起着至关重要的作用。尽管先前的研究已在个别国家的教科书中发现了性别不平等现象，但很少有研究从跨文化的角度审视这一问题。本研究应用自然语言处理方法，对来自7个文化圈、22个国家的英语教科书中的性别不平等进行了量化分析。度量指标包括角色数量、优先提及（即哪种性别首先被提及）以及基于性别的TF-IDF词汇关联。分析还识别了TF-IDF词表中出现的专有名词的性别模式，测试了大语言模型是否能区分性别化词表，并利用GloVe词嵌入来检验关键词与每种性别的关联紧密程度。结果显示，在数量、优先提及和命名实体方面，男性角色均存在一致的过度表征。所有地区均表现出性别不平等现象，其中拉丁文化圈的差异最小。