# `caption_general` — the emotion gate and the GEND/BKGN polarity

Regenerated corpus-wide on 2026-08-23 over **165,516,420 rows / 48,556 shards**.
The previous string is preserved verbatim in **`caption_general_v1`** on every row,
so the change is fully reversible and auditable.

## Emotion clause — percentile gate, not an absolute threshold

The old clause named an emotion when its raw Empathic-Insight score cleared an
**absolute** `emo_thr = 1.0`. The 40 heads are not on a common scale, so that gate
reported the scale of the head rather than the emotion of the clip:

| head | median | zeros | named before |
|---|--:|--:|--:|
| `emo_Interest` | 2.082 | 0.0 % | **90.6 % of all rows** |
| `emo_Concentration` | 1.543 | 6.1 % | 71.0 % |
| `emo_Infatuation` | −0.017 | 87.7 % | 0.3 % |
| `emo_Bitterness` | 0.012 | 5.5 % | 0.1 % |

**The rule now:** an emotion is named when it falls in the **top 10 % for that
emotion** (U ≥ 0.90), at most **3** named, ranked by percentile. A clip that
clears nothing says **"no dominant emotion"** rather than being forced to pick.

The normaliser is `capnorm.npz`: a **pooled, tie-aware mid-rank ECDF** over
**132,833,726 utterances** (`traj2/stats/globalhist.npz`, 130,785,282 rows across
8 datasets and every language, plus `vprof_vc` fitted into the same 4096 bins).
It is **not re-fitted** here — an earlier sampled-knot ECDF gave `emo_Awe` only
120 distinct knots and made it the "peak emotion" of 23 % of clips.

**Pooled, deliberately not per-dataset.** Under the pooled norm the datasets
genuinely differ (mls 0.615, evasnippets 0.608, eurospeech 0.547, emolia 0.441).
Per-dataset normalisation would force every dataset to 0.500 by construction,
erase that signal, and put captions on a different scale from the trajectory
miner, which selects chains with the same statistic.

Measured effect corpus-wide: **Interest 90.6 % → 5.3 %**, all 40 emotions now
occur, mention count 428.3 M → 341.1 M, and **17.88 %** of rows are neutral.
The neutral rate is strongly dataset-dependent and that is the intended
behaviour of a pooled norm — see the per-dataset table in `capfix/out/report.json`.

## GEND and BKGN polarity

Both ordinal ladders ran backwards relative to the data and were corrected in
`caption2.py` on 2026-08-22 (md5 `ec55b223…`); this pass applied the correction to
the corpus rows.

* **High `vn_GEND` is masculine**: +0.842 with chest resonance, −0.484 with head
  resonance, −0.352 with brightness. Independently, a sibling classification of
  all 500 voice profiles from the numeric `vn_GEND` against each profile's own
  design-spec `card_gender` agreed on **89.2 % of 379 decided voices** — an
  inverted ladder would have scored ~11 %.
* **High `vn_BKGN` is cleaner**: +0.786 with recording quality and +0.29 with
  `qual_background_quality`, an independent model head. The old wording produced
  the self-contradictory "good recording, very noisy background"; it now reads
  "good recording, no background noise".

The corrected tag is recomputed **from `vn_GEND_bucket` / `vn_BKGN_bucket`**, not
by flipping the string, so the operation is idempotent and safe to re-run. This
mattered: the `-reann` tree (`vprof_base` + `vprof_repaired`, 28.2 M rows) had
already been regenerated with the corrected ladders and measured 100 % new
polarity, while all nine live-tree datasets measured 100 % old polarity. A blind
string flip would have re-inverted those 28.2 M rows.

## What did NOT change

Only three clauses may move: the emotion clause, the GEND token inside clause 0,
and the BKGN token in the recording clause. Delivery, timbre, speech, affect,
style, recording quality, the explicit-content flag, burst handling,
`text_with_bursts`, genuineness, blend, duration and language are carried across
byte-identical. This is asserted per shard during the rewrite and re-checked
afterwards: **`other_clause_moved = 0` over all 165,516,420 rows.**

One wording change does fall inside the emotion clause: `emo_Jealousy_and_Envy`
renders as "jealousy and envy" where the old renderer wrote "jealousy & envy".