This preprint asks whether simple persona labels produce urban-sentiment judgments that are stable, distinct across profiles and valid as proxies for human perception. Using Qwen3-VL:8B through Ollama, it crosses gender, economic status, political orientation and personality style into 24 profiles. It makes 50 calls per profile on the same 50 PerceptSent images: 60,000 attempts, of which the artifact releases 59,708 valid responses and 292 failures. The same model is also run without a persona, with and without thinking, five times per image. Repetitions under an identical profile converge strongly: across 1,200 profile-image groups, agreement with the modal class averages 0.871 and has a median of 0.98. Stability, however, is not the same as valid diversity. The global profile contrast is statistically significant under the very large row count but explains less than 1% of variation; gender has no effect, while economic status, politics and personality produce small differences. Persona-conditioned outputs also place 77.34% of predictions in the extreme Negative or Positive classes, versus 65.2-66.0% without a persona. Human agreement deteriorates from coarse polarity to five classes, and no-persona controls match or exceed persona-conditioned predictions in every published task variant. The defensible conclusion is that these four labels induce highly repeatable and somewhat more extreme outputs but add little value as synthetic human annotators in this setting. Audit identifies several material reporting issues. Figure 5 compares model predictions on 50 images against the human distribution over all 5,000 images: the cited 35.3% human extreme share is not the experimental sample, where extremes are 44.0%. Extremity bias remains, but the matched gap is about 33.3 points rather than 42.5. The paper describes five-fold cross-validation, whereas the code draws one seeded 60% subsample per image and then bootstraps images. For no-persona controls the point estimate uses three of five runs, not the all-five modal class stated in the paper. Recalculation with all five preserves the conclusion but narrows several control advantages. Agreement CSVs linked by the README also have filenames and columns incompatible with the loader, so that analysis is not reproducible without reconstruction. Finally, the 1,200 entities called agents are stateless calls over only 24 label combinations, not persistent identities or independent people. There are no humans whose attributes are matched to the labels, no hierarchical model for images and repetitions, no tests or CI, and the Ollama tag does not preserve a model-weight digest. The work demonstrates consistency of prompt compliance and a useful limitation of simple personas, not demographic realism, psychological personality or valid individual simulation.
Research question
Do four demographic and style labels produce multimodal personas whose judgments about urban scenes are reproducible within profile, differentiable between profiles, and concordant with human perception, or does the same model without persona work equally or better?