Early influences that depress children’s beliefs about their own ability can lead to lower educational achievement and persistent disadvantage. In particular, receiving negative messages about gender- and race-specific levels of ability have played a role in generating disadvantage for women and minorities. Children are particularly vulnerable to negative messages about race and gender, as their beliefs about their own capacities are highly malleable.
This project aims to improve how we estimate the extent and implications of children’s exposure to race- and gender-coded messages. The initial goal of the project will be to develop, verify, apply, and disseminate new methods of human-directed, machine-implemented content analysis focused on measuring implicit messages about race and gender in visual content. We anticipate that the tools we develop will catalyze new research in fields such as computational science and social science, and will advance our understanding of the extent of these messages and their contribution to inequality.