melissa kate
<< Little Jane

Tiny prose alive not coherent

2026-09-04, v11/scripts/chat_audit.py over all of chat_logs/. Scored the
18 checkpoints she rated by ear that day (8 keep, 8 cut with enough replies to
score) against six metrics. Result, as AUC = P(kept model ranks above cut):

metrickeptcutAUC
self-contained68.7%81.3%0.20
responsive (shares a content word with the cue)23.2%33.8%0.20
asks back15.2%20.2%0.41
near-dup looping0.0%2.1%0.38
median words18.211.20.63

Both separating metrics run BACKWARDS. The mechanism is visible by eye.
g2long (CUT, 88.6% self-contained) loops: "I shall have to go to a hotel
to-morrow"
/ "I shall go to the hotel to-morrow"; "But you'll be sorry" /
"I don't see why you should be sorry". g4ask (KEPT, 59.1%) misidentifies who
it is talking to — Mr. Dunn, Cayley, Mr. Lipscombe — but is alive: "The deuce
you are. I was thinking of nothing but the moon."

The only way to never name someone offstage is to say nothing specific, and the
only way to score high on cue-responsiveness is to parrot the cue. Both metrics
reward emptiness.

Caveat: 8 vs 8, z ~ 2.0, seven metrics tested — statistically marginal. The
qualitative contrast is the strong evidence, not the AUC.

Why: this retires the self-containedness filter (9,895 "fully clean"
conversations, sized 2026-08-30, never built) — it would have selected for
g2long. It also corrects the reading of her own words: she said "as long as
her replies mostly make sense"
, and I translated that into "no offstage
referents", which is not what she meant. The axis she judges on is ALIVE vs
DEAD, not coherent vs confused.

How to apply: do not filter or score on proper nouns, offstage referents, or
cue overlap. When proposing a metric, check it against the keep/cut set in
chat_audit.py first — a metric that cannot separate her verdict is not
measuring what she hears. Supersedes the remedy in
tiny-prose-register-is-the-defect. See also
tiny-prose-voice-concentration-flat, feedback-smaller-steps.