Fig 1.
Note: Books from the 34 countries included in our corpus are coloured in teal. This includes Afghanistan, Australia, Bangladesh, Belize, Bhutan, Dominica, Ethiopia, Guyana, India, Jamaica, Kenya, Kiribati, Lesotho, Liberia, Malawi, Maldives, Namibia, Nigeria, Pakistan, Papua New Guinea, Rwanda, Samoa, Sierra Leone, Solomon Islands, South Africa, South Sudan, Sri Lanka, St Kitts and Nevis, Tonga, Uganda, United Kingdom, United States, Zambia, and Zimbabwe.
Table 1.
Number of books, by country and grade.
Table 2.
Number of books, by country and subject.
Fig 2.
Bias in gender representation, by country and subject.
Note: These figures show the predicted mean share of gendered words that are female. This measure is first calculated for each individual book, which are then estimated as a function of country, subject, grade, and (log) book length. We exclude countries with fewer than five books in our corpus. The high-income countries are Australia, the UK, and the United States. The low and middle income countries with donor-funded books are Bhutan, Ethiopia, Guyana, Kenya, Lesotho, Liberia, Namibia, Nigeria, Papua New Guinea, Samoa, Sierra Leone, South Africa, South Sudan, Tonga, Zambia, and Zimbabwe.
Fig 3.
Bias in gender representation, by age and region.
Note: These figures show the mean share of gendered words that are female. This measure is first calculated for each individual book.
Fig 4.
Adjectives most likely to be used for each gender, by region.
Note: This figure shows the share of occurrences of adjectives used to describe people, which were used for female terms or nouns. We show here the 12 adjectives which were the most skewed toward either gender across UK/US/AUS, South Asia, and Sub-Saharan Africa, provided that they occurred at least 5 times in textbooks for each region. Shares are shown separately for each region.
Fig 5.
Verbs most likely to be used for each gender, by region.
Note: This figure shows the share of occurrences of verbs used to describe people, which were used for female terms or nouns. We show here the 12 verbs—to be precise, the lemmas of verbs—which were the most skewed toward either gender across UK/US/AUS, South Asia, and Sub-Saharan Africa, provided that they occurred at least 5 times in textbooks for each region. Shares are shown separately for each region.
Fig 6.
Similarity of gendered words to stereotypical themes.
Note: This figure shows how terms related to four themes are associated with gender terms in our embeddings. Female bias is calculated as the difference between the average cosine similarity of the theme word with the set of female gender terms and the average similarity of the theme word with the set of male gender terms. The confidence intervals are calculated as the standard deviation for this statistic, over 50 bootstrap samples, where samples are generated by sampling all sentences in our corpus with replacement.
Fig 7.
Similarity of gendered words to stereotypical themes, by region.
Note: Female bias is calculated as the difference between the average cosine similarity of the theme word with the set of female gender terms and the average similarity of the theme word with the set of male gender terms. Confidence intervals are calculated as the standard deviation for this statistic, over 50 bootstrap samples, where samples are generated by sampling all sentences in our corpus with replacement, until a sample has as many sentences as the original corpus.
Fig 8.
Co-occurrence of job occupations with gendered terms (percent female), by region.
Note: This figure lists the 15 most common occupations in our full text corpus. The x-axis shows the share of all co-occurrences of the occupation and a gendered word which are female. Shares are shown separately for South Asia, Sub-Saharan Africa, and UK/US/USA.
Fig 9.
Co-occurrence of job occupations with gendered terms, by job type and region.
Note: This figure shows the distribution of occupation categories across co-occurrences with gendered words. That is, for all co-occurrences between a male gendered word and an occupation for textbooks from South Asia, 66 percent of those co-occurrences were with managerial or professional occupations (category 1). 15 percent with service (category 2) occupations, and 18.9 percent with manual (category 3) occupations.
Table 3.
Country characteristics and bias in textbook gender representation.