Confidence-Diversity Calibration of AI Judgement Enables Reliable Qualitative Coding (2508.02029v1)

Abstract: LLMs enable qualitative coding at large scale, but assessing the reliability of their output remains challenging in domains where human experts seldom agree. Analysing 5,680 coding decisions from eight state-of-the-art LLMs across ten thematic categories, we confirm that a model's mean self-confidence already tracks inter-model agreement closely (Pearson r=0.82). Adding model diversity-quantified as the normalised Shannon entropy of the panel's votes-turns this single cue into a dual signal that explains agreement almost completely (R^2=0.979). The confidence-diversity duo enables a three-tier workflow that auto-accepts 35% of segments with <5% audit-detected error and routes the remainder for targeted human review, cutting manual effort by up to 65%. Cross-domain replication on six public datasets spanning finance, medicine, law and multilingual tasks confirms these gains (kappa improvements of 0.20-0.78). Our results establish a generalisable, evidence-based criterion for calibrating AI judgement in qualitative research.