This study examines whether audience segmentation can better preserve heterogeneity when LLMs simulate social opinion. It starts from 594 U.S. participants recruited through Prolific in October 2025, with quotas aligned to Census distributions for gender, age, and region, and assigns them to six SASSY climate segments: Alarmed, Concerned, Cautious, Disengaged, Doubtful, and Dismissive. This is a quota-aligned nonprobability sample, not a nationally representative sample; weights, response rate, full composition, and segment sizes are not reported. Llama 3.1-70B and Mixtral 8x22B answer three seven-point scales asking whether climate change is pleasant/unpleasant, favorable/unfavorable, and positive/negative. Six configurations are compared: five demographic variables; demographics plus 59 or 15 theory-selected identifiers; 15 gradient-boosting predictors; the 15 SASSY items; and its four segmentation items. Evaluation separates distributional, structural, and a dimension called predictive fidelity. The main result is qualified: no configuration wins everywhere, and adding variables does not improve performance monotonically. Against the demographic profile's average KL divergence of 4.65, Theory-15 falls to 1.10 and Theory-59 reaches 2.76, while F1 and MAE change little. Item-15 has the lowest mean KL, .47, and the closest within-group variation, SD .99 and CV .54 against human values of 1.19 and .53. Data-driven comes closest to between-group separation, with mean nEMD .18 against a human benchmark of .19, although that average is driven by Mixtral at .25 while Llama remains .11. Every configuration retains over-regularization or unstable geometry. The so-called predictive fidelity is averaged Cramer's V association between identifiers and only three outcomes; it is not out-of-sample prediction or causal evidence. Item-4 and Item-15 also supply climate attitudes to predict three closely related climate attitudes from the same questionnaire, so their advantage may reflect near-target proxies rather than general heterogeneity. The Data-driven selection reports no split, cross-validation, or holdout and therefore does not establish out-of-sample performance. The two models were chosen after comparing other candidates partly on persona consistency and response diversity, properties close to the later target; that benchmark is not reported, creating favorable-selection bias. Exact generation counts, seeds, checkpoints, serving stack, retries, parsing failures, and reproducible definitions of KL, nEMD, and classification metrics are absent. There are no intervals, bootstrap analyses, tests, or stochastic replications. CV is questionable for Likert data because it depends on the scale's arbitrary zero, while MDS/Procrustes on only six groups lacks stress or stability analysis. The most serious documentary problem is that the text repeatedly cites Appendix A and Tables A1-A10 for questions, identifiers, prompts, and full results, yet the 43-page PDF ends after the references and the official arXiv source ends at the bibliography with no appendix. No official repository, dataset, code, outputs, or supplement was found. The paper contributes a useful multidimensional framework and descriptive evidence that variable choice matters, but it does not establish that segmentation generally restores human heterogeneity and its results cannot be reproduced exactly from the available artifacts.
Research question
Can audience segmentation reduce the homogenization of social simulations with LLMs, and how do distributional, structural, and association fidelity change when varying the granularity, parsimony, and selection logic of the identifiers?