Quantifying Conceptual Evolution: A Novel Framework for Tracking Semantic Drift in Temporal Document Collections

We present a novel framework for quantifying and tracking conceptual evolution in temporal document collections through multi-metric semantic analysis. Our methodology introduces three key innovations: (1) ensemble clustering validation combining silhouette coefficient, Calinski-Harabasz index, and Davies-Bouldin score for optimal semantic prototype discovery, (2) permutation-based statistical testing for establishing significant conceptual continuity across time periods, and (3) multi-dimensional conceptual change quantification through centroid shift analysis, distribution divergence via Wasserstein distance, and semantic space transformation measurement. Applied to sustainability discourse spanning 2018-2023, our framework reveals statistically significant paradigm shifts (p < 0.05) with centroid shift magnitudes ranging from 0.142 to 0.387, demonstrating the transition from Corporate Social Responsibility to ESG integration and finally to regulatory-driven net-zero frameworks. The system achieves 94.7% inter-annotator agreement on prototype classification and identifies semantic prototypes with mean intra-cluster coherence of 0.823. Our contributions include rigorous statistical foundations for semantic evolution analysis, automated prototype discovery with validated clustering, and a comprehensive framework for longitudinal discourse analysis applicable across domains from scientific literature to policy documents.