The whole-mix-vs-stem decision (#92) was about to be called on a rigged
comparison. `fit` grades whole-mix on every mix-analyzed track (163 so far,
heading to 534) and `fit --stem` on every stem-analyzed track (24) — different
SETS and different SIZES. The unpaired numbers (calibrated LOO 0.418 whole-mix
vs 0.618 stem) screamed "whole-mix decisively wins," but most of that gap was
sample size: 163 points fit a stable affine (in-sample→LOO gap 0.005), 24 points
overfit it (gap 0.063). That's a sample-size artifact masquerading as a
mode-quality verdict — exactly the kind of premature conclusion the build-the-
katana-first discipline exists to catch.
Approach: extract the raw/in-sample/LOO scoring into a shared `_evaluate()` so
every mode is graded by identical code, then add `paired` — whole-mix vs stem
head-to-head restricted to the slugs analyzed BOTH ways (the only honest
comparison). Reports both modes' raw + calibrated-LOO V/A-err, quad-hit,
emo-hit, plus an in-sample→LOO conditioning gap with an overfit flag.
The surprise (n=26 paired, validated this run): on equal footing the verdict
nearly inverts. whole-mix cal·LOO 0.569 / quad 54% / emo 23% vs stem 0.605 /
58% / 27% — whole-mix wins distance by Δ0.037, but stem wins BOTH quadrant and
emotion-hit, and both overfit (n=26 too small). The honest #92 answer is now
"inconclusive until more stems exist," not "whole-mix decisively wins." Caveat:
the paired subset is the hard cases (first-fetched = aggressive techno/dnb/
dubstep, the high-arousal region drift + the ontology critique both flagged).
Regression: `fit` output unchanged after the refactor. Interim calibration JSON
(n=163 fit) left uncommitted — it regenerates at the full 534.
