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A framework for measuring scientific innovation integrating LLM-driven semantic reasoning and bibliometric analysis
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DOI：10.1007/s11192-026-05770-3.png)
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Abstract
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Accurately quantifying scientific innovation is a central issue in research evaluation. This paper proposes and validates a unified three-dimensional framework for measuring scientific innovation at the paper level. To address limitations of existing single-axis indicators, we conceptualize innovation as an integrated construct composed of textual novelty, scholarly impact, and citation-based disruptiveness. Methodologically, we leverage the deep semantic reasoning and comparative capabilities of Large Language Models (LLMs), augmented by a Retrieval-Augmented Generation (RAG) that retrieves relevant abstracts, to quantify textual novelty; we operationalize impact via normalized citation counts and capture disruptiveness using the CD index; these heterogeneous signals are integrated using an entropy-based weighting strategy to generate an interpretable 3D innovation metric. We empirically validate the framework on a corpus of 10,875 documents from S2ORC, evaluating convergent, discriminant, and incremental validity. Findings indicate that the proposed 3D measure outperforms single-dimension metrics in distinguishing breakthrough, disruptive and nascent research, and supports an interpretable eight-quadrant typology for innovation classification.The framework offers a practical and valuable tool to research assessment and prioritization, and provides a methodological foundation for integrating deep semantic representations with citation-topology analysis in future studies. The source code for this study is publicly available at: https://github.com/sdx5256/Scientific-Innovation_3d .
Keywords:
Innovation measurement
Retrieval-augmented generation
Large language model
Bibliometrics
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