Scores by kind of claim
Each kind of claim is scored on its own, out of 100: the share that held up, with the range it likely falls in. Fewer resolved claims give a wider range.
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Factual claims
Statements about the present or the past, checked against the record: did they hold up?
76 63 88 -
Predictions
Statements about events outside the person's control, checked once they fell due: did they come true?
Insufficient record: 1 prediction resolved, 5 needed for a score.
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Promises
Commitments to the person's or organisation's own future action, with something to deliver: was it delivered?
70 52 87
All types combined: 74 (likely 64 to 84, medium confidence), from 22 claims: 15 factual claims, 7 promises. It mixes kinds of claim in different proportions for different people, so compare people kind by kind.
GiveWell's claims
“GiveWell’s Cross-Cutting research subteam focuses on improving our grantmaking over time—it investigates the impact of the programs we fund, works to resolve our largest uncertainties, and strengthens our data collection and analysis.”
GiveWell, unsigned newsletter 30 September 2026 September 2026 Updates (The GiveWell Blog)
GiveWell's Cross-Cutting research subteam investigates the impact of funded programs, works to resolve the largest uncertainties, and strengthens data collection and analysis.
Mostly true True in substance, wrong in a detail.
The second judge would not resolve this claim; the verdict shown is the first judge's.
What would count as true or false
Judged as of 2026-09-30. True: GiveWell's own team descriptions, together with the subteam's published outputs (research pages, reports or blog posts), show a Cross-Cutting research subteam doing all three things: investigating the impact of funded programs, working on the largest uncertainties, and strengthening data collection and analysis. Mostly true: the subteam exists and does most of these. Mixed: it does about half. Mostly false: the subteam exists but its focus is largely different. False: no such subteam existed.
Evidence
- Pressure-Testing Our Research as GiveWell Grows - The GiveWell Blog GiveWell · 3 September 2026 Own record
“GiveWell’s Cross-Cutting research subteam is an important part of that effort—it investigates the impact of the programs we fund, works to resolve our largest uncertainties, and aims to improve our research. As GiveWell’s grantmaking grows, the Cross-Cutting subteam is strengthening our data collection and analysis to support that growth.”
- Looking Back to Give Better: A Webinar Recap - The GiveWell Blog GiveWell · 25 June 2026 Own record
“When programs near the end of their funding, we investigate whether a grant achieved the impact we initially projected. In 2025, our growing research capacity enabled us to expand this final step for select grants and publish comprehensive evaluations that we call “lookbacks.””
- Research Strategy: Cross-Cutting - The GiveWell Blog GiveWell · 18 December 2024 Own record
“We do this in a few ways:”
Reasoning
GiveWell's September 2026 blog post (E1) describes the Cross-Cutting subteam as investigating the impact of funded programs, working to resolve the largest uncertainties, and strengthening data collection and analysis. E3, a 2024 post on cross-cutting research strategy, describes work on uncertainty in cost-effectiveness estimates. E2 shows published 'lookback' evaluations of whether grants achieved their projected impact, though it does not attribute them to the subteam. All sources come from GiveWell itself, and the data-collection activity is supported only by E1's description rather than a specific published output, so the evidence supports most but not all elements at the level the criteria require.
Cite this claim
GiveWell, “GiveWell's Cross-Cutting research subteam investigates the impact of funded programs, works to resolve the largest uncertainties, and strengthens data collection and analysis.” (said 30 September 2026). Verdict: mostly true. Reliability API, claim c_ce4ef39121c6, method 0.1.0+7831dd9d. https://epistacklabs.com/claims/c_ce4ef39121c6
Claims without a verdict
Is a verdict here wrong, or do you speak for GiveWell? Reply on the claim's own page, with the form at its foot (how replies work). Verdicts are made automatically; how verdicts and scores are made.