In order to meet the nation’s need for a larger Science, Technology, Engineering and Mathematics (STEM) workforce, we seek to broaden the participation of underrepresented groups in data science and other STEM fields.
Bringing the capacities of Black, Indigenous, and Latina/o/x scholars to bear on the production of methodologically rigorous and innovative grant proposals will diversify and strengthen the nation’s scientific research funding apparatus.
Equipping researchers and the broader public with a critical awareness of how methodologies structure inequality and opportunity–and have also advanced processes of racialization, white supremacy, and erasure–is essential in understanding the social implications of data science within an increasingly diverse world.
Demonstrating and developing approaches of how to employ quantitative and computational methods that are situated in context, history, material social relations, and as a product of material and discursive formations. In addition, we seek to demonstrate and develop non-instrumental approaches of quantification that do not essentialize, universalize, or treat data as self-evident, while also placing more of an analytical focus on multiplicity and the marginal subject.