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Explained: How language leads to gender bias in science

If you speak a language with a high gender bias, you are more likely to associate men with career and women with family, a study of 25 languages has found. And, countries with a weaker language bias have a higher representation of women in science.

The researchers examined 25 languages for gender stereotypes that undermine efforts to support equality across career paths in STEM (science, technology, engineering and mathematics). English and Hindi were among the 25 languages. (Getty Image)

For decades, researchers have examined why women are underrepresented in science and associated fields, whether in college and university or at the workplace. Studies have found that the reason is often cultural: Girls grow up believing that boys are better at these fields, even when they are capable of excelling themselves.

A new study has now examined whether these cultural stereotypes are rooted in the languages that people speak. It has found that gender associations in a language do predict people’s implicit gender associations. In other words, the findings suggest that linguistic associations may be related to people’s implicit judgement of what women can accomplish.

The study is published in the journal Nature Human Behavior.

Words & connections


The researchers examined 25 languages for gender stereotypes that undermine efforts to support equality across career paths in STEM (science, technology, engineering and mathematics). English and Hindi were among the 25 languages.

Specifically, researchers Molly Lewis of Carnegie Mellon University (Pennsylvania) and Gary Lupyan of University of Wisconsin-Madison examined how words co-occur with women compared to men. “The implicit biases don’t come from any particular phrases. The bias that we find comes from looking at which words co-occur next to each other in a lot of text, and what words tend to have the same neighbors,” Lewis told The Indian Express by email.

The degree of gender bias in languages. (Credit: Lewis & Lupyan, 2020; Nature Human Behavior)

By training machine learning models on large corpora of texts in each language, the researchers examined, for example, how often ‘woman’ is associated with ‘home’, ‘children’ and ‘family’, whereas ‘man’ is associated with ‘work,’ ‘career’ and ‘business’.

“We find, for example, that the words ‘man’ and ‘career’ tend to co-occur with each other more often than ‘woman’ and ‘career’ in nearly all 25 languages that we looked at,” Lewis said.

Psychological bias

To quantify implicit gender bias in people, the researchers measured their performance in a psychological task called the Implicit Association Test.

The results suggested that if one speaks a language with high gender bias, then one is more likely to have a gender stereotype that associates men with career and women with family.

“Our study shows that language statistics predict people’s implicit biases — languages with greater gender biases tend to have speakers with greater gender biases,” Lupyan said in a statement.

Curiously, countries with a larger older population were found to have a stronger bias in career-gender associations. Given that India has a young population, did speakers of Hindi — the only Indian language among the 25 studied — show lower implicit bias than others? Lewis replied: “Participants in India had a relatively low bias to associate men with career and women with family on the Implicit Association Task.”

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The STEM connection

The study used a gender equality metric reported by UNESCO — the percentage of women among STEM graduates in tertiary education. It found that countries with weaker associations between men and career tended to have more women in STEM fields. However, there was no relationship between the percentage of women in STEM fields and the language’s explicit gender association measure, as quantified in the study.

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The results are correlational, although the researchers said the findings do suggest a causal influence. They also noted that the Implicit Association Test used in the study has been criticised for low reliability. They have called for additional work to explore language statistics and implicit associations with gender stereotypes.

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