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Nonlinear Dynamics of Advancement Toward the ESI Top 1‱: Decomposition and Forecasting Evidence from an Emerging
Fangqun Gao1,2, Weiyan Hao2, Yuntao Wu2
1Library, Wuhan Institute of Technology, Wuhan 430205, China.
Abstract:
Advancement toward the Essential Science Indicators (ESI) Top 1‱ is an important indicator of disciplinary competitiveness, but progress near this boundary is often nonlinear because the global threshold continues to move upward. This study develops a two-layer framework combining an Exponential Decay Model (EDM) for modeling rank trajectories and a Bivariate Logarithmic Difference Decomposition Model (BLDDM) for separating institutional citation growth from external threshold pressure. Using 13 bimonthly ESI update waves from March 2024 to March 2026, we analyze Chemistry, Engineering, and Materials Science at Wuhan Institute of Technology. The results show that the EDM outperforms a linear benchmark in all three disciplines, indicating an asymptotic pattern of advancement near the Top 1‱ boundary. The BLDDM further reveals substantial disciplinary heterogeneity: Engineering faces the strongest threshold pressure, whereas Chemistry is the most favorable near-term candidate for breakthrough. These findings suggest that ESI advancement should be understood as a moving-threshold process rather than a simple accumulation of citations.
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