moral-maps

wassname/moral-maps

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Commit Graph

Author	SHA1	Message	Date
wassname	b12770cb78	fixes, naming	2026-05-08 15:23:54 +08:00
wassname	c96d02a675	refactor	2026-05-08 15:15:14 +08:00
wassname	bcbdb9cc6f	feat: multi-label moral foundation ratings with z-scored frame averaging and human calibration - 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	2026-05-03 12:48:14 +08:00

Author

SHA1

Message

Date

wassname

b12770cb78

fixes, naming

2026-05-08 15:23:54 +08:00

wassname

c96d02a675

refactor

2026-05-08 15:15:14 +08:00

wassname

bcbdb9cc6f

feat: multi-label moral foundation ratings with z-scored frame averaging and human calibration

- 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

2026-05-03 12:48:14 +08:00