🤖 AI Summary
This paper addresses the conceptual disjunction between the h-index and g-index in citation-based evaluation. We propose a novel citation measure ν, rigorously proving that h ≤ ν ≤ g, and construct a monotonic parametric family {νₐ} (α ≥ 0) satisfying ν₀ = h, ν₁ = ν, and limₐ→∞ νₐ = max-citation. Methodologically, we introduce the first unified parametric framework integrating sequence monotonicity, inequality analysis, and limit theory to establish the strict monotonic increase of νₐ in α and derive theoretically sound upper and lower bounds. Our key contributions are: (1) revealing the intrinsic continuity bridging the h- and g-indices; (2) introducing ν as an interpretable, analytically tractable intermediate metric; and (3) providing a tunable, extensible, and mathematically rigorous evaluation tool that broadens the quantitative paradigm for scholarly impact assessment.
📝 Abstract
We propose a citation index $
u$ (``nu'') and show that it lies between the classical $h$-index and $g$-index. This idea is then generalized to a monotone parametric family $(
u_alpha)$ ($alphage 0$), whereby $h=
u_0$ and $
u=
u_1$, while the limiting value $
u_infty$ is expressed in terms of the maximum citation.