Year: 2026 | Month: June | Volume 13 | Issue 1
Defining the Boundaries: Research Objectives and Scope of an AI-Based Bibliometric Framework
Harendra Nath Roy1 and Jayati Lahiri Dey2*
DOI:10.30954/2348-7437.1.2026.9
Abstract :
Doctoral theses constitute a significant yet systematically underutilized category of scholarly output. Despite their intellectual depth and methodological rigour, most dissertations remain poorly indexed, rarely cited in commercial databases, and largely invisible to mainstream bibliometric evaluation. This paper presents a structured articulation of the research objectives and analytical scope of an AI-Based Bibliometric Framework designed to address this invisibility—with particular focus on the interdisciplinary domain of Library and Information Science (LIS) and Computer and Information Science (CIS). Six specific research objectives are defined: conceptualizing the AI-driven framework, incorporating citation context analysis using NLP, addressing limitations of traditional bibliometric indicators, designing metadata validation mechanisms, conducting comparative India–Global analysis, and contributing theoretically to AI-enabled scientometrics. The scope encompasses doctoral theses from national repositories (Shodhganga), international databases (ProQuest, EThOS, Scopus), and LIS-specific archives, with explicit AI compatibility criteria. A comparative citation analysis of 284 doctoral theses from five Indian universities (2015–2023) empirically grounds the study, revealing that 18–35% of theses carry zero recorded citations—reflecting a discoverability gap rather than low quality. The framework integrates PageRank-based importance scoring, TF-IDF and LDA-based semantic classification, context-aware citation impact metrics, and altmetric aggregation. By bridging LIS bibliometric tradition with CIS computational methodology, the proposed framework advances both the theory and practice of research evaluation in the era of AI-augmented scholarship.
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