So, randomization is not a *sufficient* condition for good research. Far from it. The best experimental social science being done is that work where either the theory or the operationalization (or both) are the emphasis. Randomization is "easy" - the challenge is what you randomize and why.

Can Large Multimodal Models (LMMs) extract features of urban neighborhoods from street-view images? New with Paige Bollen (OSU) and @joehigton.bsky.social (NYU): Sometimes, but the models better recover national assessments that local ones, even w/additional prompting (which can make things worse!)

New paper: LLMs are increasingly used to label data in political science. But how reliable are these annotations, and what are the consequences for scientific findings? What are best practices? Some new findings from a large empirical evaluation. Paper: eddieyang.net/research/llm_annotation.pdf

Great analogy to connect AI to many canonical political science questions. Political behavior has led the way in studying AI. Excited to see institutions catch up😀

If no resource constraint, what open-weight LLM would you use in your research (for data labeling, coding etc.)?

Awesome work! Love to see different approaches to this problem.

Really interesting read. Refreshing perspective.