When asked to draw a mathematician, girls are twice more likely to draw a man than a woman, while boys almost universally draw a man. A similar tendency to associate professions such as firefighters, surgeons and fighter-pilots to the masculine gender has been observed in children as young as 5 years old. Gender stereotypes form early in the child’s development and are carried over throughout adolescence into adulthood, leaving long-lasting effects on emotional and cognitive development, while shaping activity and career choices as well as impacting academic performance.
In this work, we propose a solution for addressing gender under- and misrepresentation in textual literature for pre- and primary-school children. In children’s books, a crucial element in the child development process, male characters outnumber female characters, non-binary characters are basically absent, and gender roles and stereotypes are being reinforced.
The goal of the project is twofold. Firstly, we want to identify and measure different aspects related to gender under- and misrepresentation. For example, proportion of male vs. female characters, gender-assuming pronouns and language that reinforces stereotypical gender roles could all be relevant in this context. Once reliable measurements are obtained, they will be combined into a gender representation score. The score should be easily interpretable to increase public awareness and serve as an aid to parents, educators and decision-makers. Secondly, after computing this score we want to develop best-practice guidelines for its validation in order to ensure transparency and accuracy of the methodology. In this step we will rely primarily on the opinions of gender experts and linguists.
The innovative character of this project lies on the integration of the following quantitative and qualitative research techniques. On the one hand, the measurement procedure will build on modern artificial intelligence (AI) algorithms for the analysis of text. Recent advances in this field allow for algorithms to be aware of the context in which words appear, rather than analyzing words separately. Context-awareness makes such algorithms promising tools for the measurement of more complex components of gender bias in textual data. However, as it is well known that AI techniques may present drawbacks in terms of transparency and interpret ability, we do not plan to rely solely on them in our analysis. In particular, we will complement them by making use of state-of-the-art qualitative methods for literature review, data collection and validation procedure.
