🚨 ACCEPTED AT SMR 🚨 Confused by colleagues who seem to want to study LLMs instead of humans? Frustrated by skeptics (e.g., myself 8 months ago) who dismiss LLMs as a potential source of data on human behavior? Check out our paper for a new way forward: osf.io/j3bnt_v3/

Mixed feelings about silicon subjects (LLM predictions of human behavior) as replacements for human subjects? Consider the mixed subjects design. 🚨Now published at Sociological Methods and Research🚨 doi.org/10.1177/004912…

Our article on using LLMs for text classification is out now in SMR! We compare different learning regimes, from zero-shot to instruction-tuning, and provide recommendations for using these models for sociologists and other social scientists. doi.org/10.1177/004912…

How can we leverage generative AI to advance social science methods and research? Daniel Karell and Thomas Davidson led a special issue in Sociological Methods & Research to find out. Special kudos to them! journals.sagepub.com/doi/10.1177/00…

🚨Just a month until the Computational Linguistics for the Political and Social Sciences (CPSS) workshop deadline (June 13, AoE) #KONVENS2025. Super excited for the upcoming round and can’t wait to see your submissions ✨ cpss-sig.github.io/CPSS-2025/inde…

Thanks to Jessica Hullman for a thoughtful dive into our Mixed Subjects Design paper with Michael Howes and Austin van Loon on the Stat Modeling blog! Read the discussion ➡️ statmodeling.stat.columbia.edu/2025/06/12/bet…

Excited to see our Centaur project out in nature. TL;DR: Centaur is a computational model that predicts and simulates human behavior for any experiment described in natural language.

Excited to continue learning about the latest #CSS at IC2S2! I’ll be at the Social Prediction Session, presenting the mixed subjects design on combining human and LLM data in experiments. Paper with Michael Howes and Austin van Loon. Come join us! doi.org/10.1177/004912…