Scientific Gap Index (SGI): An Innovative AI-Assisted Bibliometric Metric for Scientific Research
Keywords:
bibliometrics, scientometrics, artificial intelligence, VOSviewerAbstract
The identification of scientific gaps constitutes a recurring challenge in bibliometrics and scientometrics, as it often relies on subjective interpretations derived from literature reviews. The objective was to propose the Scientific Gap Index (SGI), an innovative bibliometric metric co-assisted by artificial intelligence to estimate the potential for generating new knowledge within a scientific domain. A documentary literature review was conducted between 2022 and 2026, alongside a conceptual analysis supported by VOSviewer. The SGI integrates structural indicators widely recognized in scientific network analysis: centrality, modularity, density, and normalized link strength. Its formulation expresses the imbalance between the forces of cognitive expansion and the forces of epistemological consolidation present in a scientific network. The results suggest that the index enables the identification of emerging areas, the quantification of scientific gaps, and the support of research decision-making. It represents an original proposal with the potential to strengthen prospective bibliometric studies.
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