Researchers examined gender stereotypes baked into 25 languages to explore why fewer women enter science, technology, engineering and mathematics fields.

Molly Lewis, special faculty at CMU and her research partner, Gary Lupyan, associate professor at University of Wisconsin-Madison, set out to examine the effect of language on career stereotypes by gender. They found that implicit gender associations are strongly predicted by the language we speak. Their work suggests that linguistic associations may be causally related to people’s implicit judgement of what women can accomplish.

«Young children have strong gender stereotypes as do older adults, and the question is where do these biases come from,» said Lewis, first author on the study. No one has looked at implicit language — simple language that co-occurs over a large body of text — that could give information about stereotypical norms in our culture across different languages.»

In general, the team examined how words co-occur with women compared to men. For example, how often is ‘woman’ associated with ‘home,’ ‘children’ and ‘family,’ where as ‘man’ was associated with ‘work,’ ‘career’ and ‘business.’

«What’s not obvious is that a lot of information that is contained in language, including information about cultural stereotypes, [occurs not as] direct statements but in large-scale statistical relationships between words,» said Lupyan, senior author on the study. «Even without encountering direct statements, it is possible to learn that there is stereotype embedded in the language of women being better at some things and men at others.»

They found that languages with a stronger embedded gender association are more clearly associated with career stereotypes. They also found that a positive relationship between gender-marked occupation terms and the strength of these gender stereotypes.

Story Source: Materials provided by Carnegie Mellon University. Original written by Stacy Kish. Note: Content may be edited for style and length.