Digital Humanities

by Sarah Daniel. Researchers increasingly recognize that neighborhoods profoundly shape life outcomes, yet measuring them remains challenging. A common approach uses administrative boundaries, such as census tracts, as proxies for neighborhoods, but this method presents three key challenges. First, administrative boundaries may fail to capture residents’ lived experiences, a limitation that is particularly concerning in marginalized communities; second, they can misrepresent contextual effects; and third, they may produce inconsistent findings. To address these issues, I advocate for the use of self-defined neighborhood boundaries as an alternative measure. I compare GIS- and non-GIS-based methods and propose that GIS-based methods offer the strongest potential for more valid measurement.

by Paige Park. Our capacity to accurately predict social outcomes is increasing. Deep neural networks and artificial intelligence are crucial technologies pushing this progress along. As these tools reshape how social prediction is done, social scientists should feel comfortable engaging with them and meaningfully contributing to the conversation. But many social scientists are still unfamiliar with and sometimes even skeptical of deep learning. This tutorial is designed to help close that knowledge gap. We’ll walk step-by-step through training a simple neural network for a social prediction task: forecasting population-level mortality rates.