Zhilong Zhao

Zhilong Zhao, Yindi Liu

LLMs enable qualitative coding at large scale, but assessing reliability remains challenging where human experts seldom agree. We investigate confidence-diversity calibration as a quality assessment framework for accessible coding tasks where LLMs already demonstrate strong performance but exhibit overconfidence. Analysing 5,680 coding decisions from eight state-of-the-art LLMs across ten categories, we find that mean self-confidence tracks inter-model agreement closely (Pearson r=0.82). Adding model diversity quantified as normalised Shannon entropy produces a dual signal explaining agreement almost completely (R-squared=0.979), though this high predictive power likely reflects task simplicity for current LLMs. The framework enables a three-tier workflow auto-accepting 35 percent of segments with less than 5 percent error, cutting manual effort by 65 percent. Cross-domain validation confirms transferability (kappa improvements of 0.20 to 0.78). While establishing a methodological foundation for AI judgement calibration, the true potential likely lies in more challenging scenarios where LLMs may demonstrate comparative advantages over human cognitive limitations.

Zhilong Zhao, Yindi Liu

Automated content analysis increasingly supports communication research, yet scaling manual coding into computational pipelines raises concerns about measurement reliability and validity. We introduce a Hierarchical Error Correction (HEC) framework that treats model failures as layered measurement errors (knowledge gaps, reasoning limitations, and complexity constraints) and targets the layers that most affect inference. The framework implements a three-phase methodology: systematic error profiling across hierarchical layers, targeted intervention design matched to dominant error sources, and rigorous validation with statistical testing. Evaluating HEC across health communication (medical specialty classification) and political communication (bias detection), and legal tasks, we validate the approach with five diverse large language models. Results show average accuracy gains of 11.2 percentage points (p < .001, McNemar's test) and stable conclusions via reduced systematic misclassification. Cross-model validation demonstrates consistent improvements (range: +6.8 to +14.6pp), with effectiveness concentrated in moderate-to-high baseline tasks (50-85% accuracy). A boundary study reveals diminished returns in very high-baseline (>85%) or precision-matching tasks, establishing applicability limits. We map layered errors to threats to construct and criterion validity and provide a transparent, measurement-first blueprint for diagnosing error profiles, selecting targeted interventions, and reporting reliability/validity evidence alongside accuracy. This applies to automated coding across communication research and the broader social sciences.