The rapid evolution of AI technologies, exemplified by BERT-family models, has transformed scientific research, yet little is known about their production and recognition dynamics in the scientific system. This study investigates the development and impact of BERT-family models, focusing on team size, topic specialization, and citation patterns behind the models. Using a dataset of 4,208 BERT-related papers from the Papers with Code (PWC) dataset, we analyze how the BERT-family models evolve across methodological generations and how the newness of models is correlated with their production and recognition. Our findings reveal that newer BERT models are developed by larger, more experienced, and institutionally diverse teams, reflecting the increasing complexity of AI research. Additionally, these models exhibit greater topical specialization, targeting niche applications, which aligns with broader trends in scientific specialization. However, newer models receive fewer citations, particularly over the long term, suggesting a "first-mover advantage," where early models like BERT garner disproportionate recognition. These insights highlight the need for equitable evaluation frameworks that value both foundational and incremental innovations. This study underscores the evolving interplay between collaboration, specialization, and recognition in AI research.

翻译：以BERT系列模型为代表的AI技术快速发展已彻底改变了科学研究，然而科学体系中这些模型的生产与认可机制仍鲜为人知。本研究通过分析模型背后的团队规模、主题专业化和引用模式，深入探讨BERT系列模型的开发历程与学术影响。基于Papers with Code（PWC）数据集中4,208篇BERT相关论文，我们追踪了BERT系列模型在方法学代际间的演进路径，并探究模型新颖性与其生产及认可度的关联。研究发现：较新的BERT模型由规模更大、经验更丰富且机构多元化的团队开发，这反映了AI研究日益增长的复杂性；同时这些模型展现出更强的主题专业化倾向，专注于细分领域应用，这与科学领域普遍的专业化趋势相符。然而较新模型获得的引用量更少（长期表现尤为明显），暗示着"先发优势"的存在——早期模型（如原始BERT）获得了不成比例的学术认可。这些发现表明，需要建立更均衡的评估体系以同时重视基础性突破与渐进式创新。本研究揭示了AI研究中合作模式、专业化程度与学术认可之间不断演变的互动关系。