Calibrating Synthetic Respondents: A Within-Persona Experiment on Public Reactions to Protest in Turkey

Large language models are increasingly used to simulate survey respondents, yet the inference pipeline that turns a persona into a response is rarely calibrated for the population and outcome it is applied to. This study makes population- and outcome-specific calibration the research task itself. Using a nationally representative survey in Turkey as the benchmark, we construct persona-conditioned synthetic respondents for all 2,615 survey participants and search a structured space of persona content, sampling strategies, temperatures, model backbones, and prompt framings. Calibration is assessed against two held-out outcomes that carry the response structures on which social movement research typically rests, a rare binary measure of protest participation and a five-point attitude item, and the selected protocols differ between the two. We then run a within-persona synthetic experiment that places the same protest for Kurdish-language education in a political opportunity and a political threat context. The threat context lowers simulated movement legitimacy and willingness to attend, and the simulated contrasts differentiate by political orientation, ethnicity, and education in the directions theory anticipates. Calibration is a precondition for reading synthetic experiments, not a robustness check appended to them.