Leveraging multiple digital footprint datasets to predict racial, sex-based, and sexual-orientation bias across US states - Raphael Derecki; Brian O'Shea; James Goulding (International Journal of Population Data Science, 2024)
Introduction & Background Racial, gender, and sexual-orientation biases are pervasive throughout society. Importantly, modern digitally oriented datasets can elucidate important societal variables and potential solutions. One contemporary theory that attempts to explain these biases is parasite-stress: an evolutionary psychology hypothesis suggesting that increased infectious diseases rates increase out-group biases. We present preliminary findings that suggest that disease rates are a meaningful geospatial predictor of multiple biases.