Auditable Authenticity: A Four-Domain Conjunctive Rule for Chinese White Porcelain, Benchmarked Against Machine Learning on Published Archaeometric Data
This paper proposes an auditable, non-compensatory conjunctive rule for authenticating Chinese white porcelain across four evidence domains that, while slightly less accurate than black-box machine learning models, achieves comparable performance when restricted to calibrated high-confidence predictions, thereby offering a transparent alternative that exposes the corroboration structure of every judgment.