TL;DR
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 results show that current LLMs are not reliable for unsupervised STI indicator workflows, and Hybrid human-AI approaches are recommended as a near-term alternative.
Predicting the future impact of a newly published paper is challenging because it must be inferred from heterogeneous evidence available at publication time. Existing approaches often rely on a single source of information or combine multiple sources without accounting for their different predictive roles. In this pape...
Research on e-government and digital transformation is widely seen as conceptually fragmented. Existing literature reviews address this through either systematic literature reviews (SLRs), which offer depth but risk selection bias, or bibliometric methods, which scale well but yield generic, low-resolution findings. Th...
To the authors' knowledge, this is the first platform integrating clustering based recommendation with RAG based querying for regional innovation monitoring, offering a replicable design for similar settings.
This hybrid method provides a reproducible, scalable, open-source approach for automated cluster assessment in SLRs and will be implemented in the EmbedSLR open-source software.
INTRODUCTION The evaluation of individual scientific contribution remains a major challenge in research assessment and academic training. Traditional bibliometric indicators, such as the h-index and Journal Impact Factor, rely heavily on citation counts and jornal prestige, neglecting key dimensions including auth...
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