1.1.0 — Tighten the measurement

Hardens the measurement around the v1.0.0 classifier. The classifier's
happy-path behavior is unchanged; this release is about trusting the numbers.

Added

• Macro-F1 — the honest single number for an imbalanced problem
  (category macro-F1 0.765 vs 79.0% accuracy; domain 0.973).
• Stability harness (src/stability.py) — sizes the run-to-run noise floor
  (category accuracy std 0.24 pts over 5 runs), so a config change counts only if
  it clears ~2× std. Confirms the reverted prompt experiment's 2.3-pt regression
  was real (~10× the noise), not luck.
• Error audit (evals/error_audit.md) — all 67 misses triaged; ~90% are
  label-scheme overlap (industry/procurement/technology), not model error.
• Enum-validation guard in classify() — re-samples once on an out-of-enum
  label, then raises. Caught a real defect: 1/300 predictions returned an
  invalid category.

Fixed

• Corrected the docs claim that tool-use "rejects out-of-enum output at the API
  layer." It's a guided prior, not a hard constraint; enum membership is now
  validated in code.

Docs

• docs/CASE_STUDY.md narrative writeup; docs/how-it-works.md plain-language
  one-pager with a precision/recall/F1 definition and a "Threats to validity"
  section.

Full details in CHANGELOG.md.