Leveraging on large language model to classify sentences: a case study applying STAGES scoring methodology for sentence completion test on ego development

While this suggests valuable scoring that leverages large language models, it also highlights the opportunity for further refinement to closely match expert assessments. We observed low variability in aggregated values, demonstrating that the automated process functions effectively at scale. The robustness of aggregated data is particularly evident when calculating ego development scores for individuals, groups, corporate units, and entire corporations. This capability underscores the utility of the automated system for high-level evaluations and decision-making leveraging on a solid indicator. While the classification system developed in this case study shows promise, targeted enhancements may help to achieve a level of accuracy and reliability that improves alignment with experts' evaluations for single sentences. The methodology developed in this case study appears to be useful to support other evaluations at scale that leverage large language models using other maps of classifications.