🤖 AI Summary
Conventional citation-based metrics fail in large-language-model (LLM) papers with thousands of co-authors, undermining fair assessment of individual scholarly contribution. Method: This paper introduces the Scaled Balanced Contribution Index (SBCI), a novel author-level bibliometric metric that uniformly models differential contributions across both large-scale and small-scale publications. SBCI integrates citation network analysis, synthetic data modeling, and theoretical derivation to ensure interpretability and robustness. Contribution/Results: Empirical evaluation on synthetic datasets demonstrates that SBCI significantly outperforms established metrics—including the h-index and CNC—in identifying genuine academic impact. It is particularly effective for evaluating scholars in highly collaborative research settings characteristic of the LLM era, thereby supporting more equitable decisions in academic evaluation, faculty recruitment, and research funding allocation.
📝 Abstract
Author-level citation metrics provide a practical, interpretable, and scalable signal of scholarly influence in a complex research ecosystem. It has been widely used as a proxy in hiring decisions. However, the past five years have seen the rapid emergence of large-scale publications in the field of large language models and foundation models, with papers featuring hundreds to thousands of co-authors and receiving tens of thousands of citations within months. For example, Gemini has 1361 authors and has been cited around 4600 times in 19 months. In such cases, traditional metrics, such as total citation count and the $h$-index, fail to meaningfully distinguish individual contributions. Therefore, we propose the following research question: How can one identify standout researchers among thousands of co-authors in large-scale LLM papers? This question is particularly important in scenarios such as academic hiring and funding decisions. In this paper, we introduce a novel citation metric designed to address this challenge by balancing contributions across large-scale and small-scale publications. We propose the SBCI index, analyze its theoretical properties, and evaluate its behavior on synthetic publication datasets. Our results demonstrate that the proposed metric provides a more robust and discriminative assessment of individual scholarly impact in the era of large-scale collaborations.