- Add scripts/07_multilabel.py: LLM judge rates all 7 foundations per vignette
using violation (forward) and acceptability (reverse) frames
- Foundation definitions drawn from Clifford et al. (2015) survey rubric
- Z-score each frame per foundation before averaging to cancel range bias
- Calibrate LLM Likert → human % via per-foundation OLS (classic set only)
- Add scripts/07a_merge_labels.py: merges llm_* and calibrated_* into vignette files
- Update README and HF dataset card with methodology and calibration quality table
- Classic set: 80.3% dominant-foundation accuracy, Pearson r 0.69-0.89 per foundation
Was emitting `logger.warning("pmass=0.XX<0.9 — top-5: ...")` per-row, which
spammed the log heavily during heavy-steering eval (many rows go OOD at once).
Now collects all low-pmass rows in the batch and emits one summary line with
the worst-case top-5, e.g.:
pmass<0.9 on 7/16 rows in this batch; worst=0.412 top-5: '1'=0.40, ...
Same diagnostic signal, ~16× fewer log lines per batch.
