card: block 5 — per-scope movement on both slices

README.md

@@ -72,6 +72,9 @@ something the others hid:
 * **4 — composed.** Horos never runs alone; a head-only number is not a product number.
   It is also where an unqualified 0.500-vs-0.495 claim went out on this card and turned
   out to be inside the noise (see block 4).
+* **5 — per-scope.** Every other block is an average or a count, and both let a gain on
+  one scope pay for a regression on another without saying so. Only this view names the
+  scope that got worse.
 
 `swallowed` and `escalated` stay separate throughout: they have opposite fixes, and
 collapsing them into "not answered" hides which one you have.
@@ -97,6 +100,8 @@ column is the same benchmark's LLM baseline.
 | **disjoint rate** | 0.206 | 0.217 | — | ≤0.03 ❌ |
 | per-scope recall ≥ 0.60 | 6 / 14 | 2 / 14 | 9 / 14 | 14/14 ❌ |
 
+Which six, and which scope went backwards: block 5.
+
 ### 2. Real language — a gap finder, not a score
 
 53 hand-annotated natural phrasings neither version trained on. The labels are one
@@ -157,6 +162,68 @@ Note the direction of the v1 → v2 trade in this table: composed accuracy up ~7
 composed negatives-abstained down ~6. The escalation path was covering for the head's
 false-positives, and v2 hands it less to cover.
 
+### 5. Per-scope — where it moved, and where it didn't
+
+Blocks 1 and 3 report counts ("6 / 14 above the floor", "routed 0.565"). A count cannot
+be audited: it says how many scopes cleared the bar and never which, so a large gain on
+one scope silently pays for a regression on another. Both slices, sorted by movement.
+
+**Unseen phrasings** — did the gains reach language nobody wrote down?
+
+| scope | n | v1 | v2 | Δ |
+|---|---|---|---|---|
+| `places` | 217 | 30% | 68% | **+37** |
+| `schedule` | 65 | 31% | 65% | **+34** |
+| `work_context` | 420 | 22% | 51% | **+29** |
+| `relationship_context` | 174 | 16% | 42% | **+26** |
+| `public_bio` | 229 | 67% | 85% | +17 |
+| `messages` | 253 | 49% | 66% | +17 |
+| `attention` | 145 | 35% | 46% | +10 |
+| `complexity` | 178 | 39% | 47% | +8 |
+| `activity` | 308 | 61% | 68% | +7 |
+| `resources` | 151 | 28% | 34% | +6 |
+| `availability` | 116 | 21% | 25% | +4 |
+| `contacts` | 119 | 76% | 73% | −3 |
+| `ai_conversations` | 111 | 42% | 39% | −4 |
+| `health` | 259 | 61% | 56% | **−5** |
+
+**Gate benchmark** — this is what "6 / 14 above the floor" expands to.
+
+| scope | n | v1 | v2 | Δ | ≥0.60 |
+|---|---|---|---|---|---|
+| `contacts` | 58 | 69% | 83% | +14 | ✅ |
+| `public_bio` | 54 | 67% | 81% | +15 | ✅ |
+| `attention` | 73 | 25% | 71% | **+47** | ✅ |
+| `schedule` | 59 | 44% | 69% | +25 | ✅ |
+| `activity` | 78 | 58% | 64% | +6 | ✅ |
+| `messages` | 61 | 51% | 64% | +13 | ✅ |
+| `health` | 116 | 47% | 54% | +8 | ❌ |
+| `places` | 61 | 26% | 49% | +23 | ❌ |
+| `availability` | 62 | 35% | 45% | +10 | ❌ |
+| `resources` | 70 | 27% | 37% | +10 | ❌ |
+| `complexity` | 82 | 35% | 37% | +1 | ❌ |
+| `ai_conversations` | 54 | 43% | 35% | **−7** | ❌ |
+| `relationship_context` | 77 | 17% | 34% | +17 | ❌ |
+| `work_context` | 73 | 25% | 30% | +5 | ❌ |
+
+Three things only this view shows:
+
+* **`ai_conversations` is down on both slices** (−7, −4) — the one unambiguous regression,
+  not a slice artifact. v2's training targeted artifact-concrete phrasings, and questions
+  about your own past AI conversations are the scope least like an artifact.
+* **`health` and `contacts` flip sign between slices.** `health` gains 8 on the benchmark
+  and loses 5 on unseen phrasings; `contacts` gains 14 and loses 3. The two instruments
+  measure genuinely different things, and a card publishing only one of them would report
+  either as a clean win.
+* **The four biggest unseen gains are exactly the four scopes v2's authoring targeted**
+  (`places`, `schedule`, `work_context`, `relationship_context`, +26 to +37). That is the
+  evidence the authoring generalised rather than being memorised — the gains land on
+  phrasings of those scopes that nobody wrote down.
+
+Floors are still unmet: on the gate slice, 8 of 14 scopes sit under the 0.60 recall bar.
+`work_context` (30%), `complexity` (37%) and `resources` (37%) are the weakest, and
+`relationship_context` at 34% remains the hardest scope in the taxonomy.
+
 ## Limitations
 
 * **Confident-none swallowing, now concentrated rather than general.** Overall dead rate
@@ -175,8 +242,12 @@ false-positives, and v2 hands it less to cover.
   knowing *which* of your data are separate abilities; this round only advanced the
   first. The escalation path absorbs less of that than it used to — composed
   negatives-abstained fell 0.972 → 0.909.
-* **8 of 14 scopes are under the 0.60 recall floor** (v1: 12). This artifact has not
-  cleared its promotion gate; it fronts an LLM in shadow/advisory postures only.
+* **8 of 14 scopes are under the 0.60 recall floor** (v1: 12), weakest `work_context`
+  30%, `relationship_context` 34%, `complexity` and `resources` 37%. This artifact has
+  not cleared its promotion gate; it fronts an LLM in shadow/advisory postures only.
+* **`ai_conversations` regressed on both slices** (−7 gate, −4 unseen) — the one scope
+  v2 made unambiguously worse. Questions about your own past AI conversations are the
+  scope least like the artifact-concrete register v2 was trained to fix.
 * All numbers are synthetic-benchmark. Real-traffic behaviour is being measured in
   shadow mode.
 * English only. **No user data, ever** — the loader refuses artifacts whose manifest