urn:uuid:b67bf997-1db7-5382-8377-0e19da95dd4f New versions of this research opportunity and changes to the claims it rests on. 2026-10-21T00:00:00Z SocioLens urn:uuid:7c7b070b-ecca-5b10-8552-b26d4beb8e3b 2026-10-21T00:00:00Z

What changed: The correlation is no longer held back. The main result now gives it: across 469 effects from 70 experiments, GPT-4-derived predictions correlated with the original effect sizes at r = 0.85 (95% CI 0.81–0.88), and at r = 0.92 (95% CI 0.89–0.94) after the authors’ adjustment for sampling error in the original estimates. The limitations now say that these figures come from the authors’ accepted manuscript, not from the Nature version of record. The public-facing claim, the source, the scope, the verification state and the publication status are unchanged; the verification state is the one recorded for version 1.0 and does not cover the two correlations, which were read in the manuscript only. Why: Version 1.0 held back the exact r=.85 figure and its attribution to the Nature version of record (marked HOLD in the R03 LD-07 Canonical Claim Ledger v1.0), because the exact figure had not been extracted line by line from the Nature article. On 21 October 2026 the project owner supplied the authors’ accepted manuscript of the paper, as posted on OSF, which states that it has been accepted for publication in Nature. Its page 4, under "Primary test archive", reports: "In an analysis of 469 effects from 70 experiments, GPT-4-derived predictions were strongly correlated with original effect sizes (r = 0.85, 95% CI = [0.81, 0.88]; r_adj = 0.92, 95% CI = [0.89, 0.94])". The Nature version of record (656:115–122) was still not read, because the publisher blocks the project’s tools, so this version does not claim that the published article prints the same figures; the project owner chose to publish them with that attribution rather than wait for the version of record. The copy read was a print of the manuscript without a text layer, so the figures were read from the page image. The check and this revision were done by AI agents reading the manuscript, not by a person.

urn:uuid:92b2d69e-d187-5fc5-b1a1-0bff07c85110 2026-10-13T00:00:00Z

Literature check added: a second, deeper search for existing work, with its queries, new nearest work and limits. The question and verdict stand.

urn:uuid:4be1feeb-ce99-5e3b-b43a-9421db354a8b 2026-10-12T00:00:00Z

Using Afrobarometer, Arab Barometer and Latinobarómetro items fielded after model training cutoffs, does synthetic-sample accuracy at country, subgroup and individual level depend on interview language (for example Swahili or Hausa versus English, Arabic versus English), and do LLM codes of political constructs in low-resource languages pass a replacement test against trained in-country coders?

urn:uuid:e45e89a7-6dcd-5688-9188-341e77865a9f 2026-10-07T00:00:00Z

Pew Research Center (30 September 2026) built an AI “digital twin” for each panelist analyzed from three 2026 waves of its American Trends Panel, a nationally representative panel of randomly selected U.S. adults. Each twin was conditioned on that person’s profile and earlier survey answers, and weighted synthetic and human results were compared on nearly 300 questions. With its best-performing configuration (Claude Opus 4.6), synthetic estimates differed from human estimates by an average of 12.4 percentage points. Error was substantial for every subgroup reported, reaching 16.1 points for Republicans and Republican leaners and 15.1 points for Black adults. On 47% of questions at least one answer choice was selected by no synthetic respondent, which happened on no question among humans. Results also differed systematically by AI model, although some toplines matched closely. This strengthens the evidence for LD07-HQ-C06 on selected-average agreement, real-world response variance and subgroup heterogeneity. The report contains no regression or treatment-effect analysis and no individual-level accuracy result for its final configuration, so those parts of the claim are unchanged. The claim wording is unchanged.