Claim LD07-HQ-C05 (v1.1)

Provenance and status

Claim type

Fact

Publication status

Eligible for publication, with qualifiers: estimand

Verification state

Checked against the primary source; cleared with a source-specific qualifier (recorded as “CLEARED WITH QUALIFIER”). Who checked this claim is not recorded for it; claim checks so far were AI-led, and no person has checked claims against their sources.

Does not license

Synthetic population substitution; unbiased effect sizes; mechanism recovery; subgroup validity; non-US transport

Limitations

US survey-experiment archive; the correlations in the main result are taken from the authors’ accepted manuscript (posted on OSF), not checked against the Nature version of record

Source (as cited)

Ashokkumar, Hewitt, Ghezae & Willer, Nature 656:115–122 (2026), doi:10.1038/s41586-026-10742-x

Source type

Peer-reviewed journal article (recorded as “Peer-reviewed journal article”)

Institutional position

Independent academic; one author also industry-affiliated

Maturity of the evidence

Contested evidence (positive bounded use)

Recorded date

21 October 2026

Currentness / correction

This is the current version. No correction or concern is recorded on its sources.

Sources this version cites

• Ashokkumar, Hewitt, Ghezae & Willer, “Large language models can predict the results of social science experiments,” Nature 656:115–122 (2026) (Peer-reviewed journal article)
  Read the original

Version

This page shows version v1.1. Its record never changes, even when newer evidence arrives; the site may add a note to the page or withhold a figure not cleared for public use.

This pinned version is also the current version.

Versions: v1.0, v1.1 · current

What changed in this version

This version supersedes version v1.0 (LD07-HQ-C05--v1.0), which stays available with its record unchanged.

Changed fields

14 MAIN RESULT; 18 LIMITATIONS; 25 RECORDED DATE

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.