Liu, Kaneko & Chu's present-skew and Kumagae & Kaneko's respondent-coverage-gap measure different mathematical objects, built on a confirmed shared dataset and author

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Liu, Kaneko & Chu's present-skew result and Kumagae & Kaneko's respondent-coverage-gap (RCG) resemble each other closely enough to invite confusion, but the two papers compute different statistics over different objects. Liu, Kaneko & Chu never generate individual synthetic respondents: they elicit one verbalized opinion distribution per question directly from a model and compare it, as a whole, against a country's aggregate human WVS distribution ("We calculate human opinion distributions from the statistical data of the WVS, which contain more than 1,000 pieces of human data for each surveyed country"), scored with a reused metric ("we use the alignment proposed by Santurkar et al. (2023) as our metric") that Kumagae & Kaneko classify, by name, under what they call "marginal fidelity" — comparing "answer distributions question by question, without pairing generated and human respondents." RCG instead requires silicon-sampled individual respondents and measures each one's complete answer-vector distance to the nearest real human's ("represents each respondent as a point defined by their complete answer profile and measures how far each human respondent is from the nearest generated one").

The resemblance between the papers is not coincidental: Kumagae & Kaneko's WVS evaluation directly reuses Liu et al.'s own question set ("uniformly sample 100 questions from the 144 used by Liu et al. (2026)"), and both share co-author Masahiro Kaneko (MBZUAI, confirmed by matching affiliation and email). Yet Kumagae & Kaneko explicitly mark Liu et al.'s multilingual scope as a separate, out-of-scope axis from their own US-respondent-level question ("Non-US and multilingual settings... are also outside our analysis") — a first-party signal that the two papers see themselves as asking different questions, not restating one finding.