近日，复旦大学国家智能评价与治理基地赵星教授团队博士研究生贺云帆以第一作者身份在图书情报领域国际顶级期刊Journal of Informetrics（SSCI收录）发表题为《基于共词网络的生成式人工智能与基因编辑前沿领域政策焦点识别》（Identifying the policy foci via co-word networks in the frontier fields of GenAI and GeneEdit）的研究论文。该研究基于Overton政策文本，构建GenAI与GeneEdit共词网络，并以双重h型截断识别政策热点与焦点。为前沿技术政策议题识别提供了可复现、可解释的量化方法，有助于提升政策分析效率与决策支持能力。

摘要：

Understanding policy priorities in rapidly evolving technological domains requires analytical approaches that can capture thematic structures and their evolution over time. This study employs co-word network analysis to identify and compare the policy foci of two frontier fields: Generative Artificial Intelligence (GenAI) and Gene Editing (GeneEdit). Drawing on policy documents in the Overton database, we construct co-word networks in which nodes represent key concepts and edges indicate their co-occurrence relationships. With definitions of one-order h cutoff as policy hotspots and two-order h-cutoff as policy foci in co-word networks involving a total of 573 GenAI and 363 GeneEdit policies, we found that there were 23 policy hotspots and 4 policy foci in GenAI policies, and that there were 14 policy hotspots and 7 policy foci in GeneEdit policies. As validated by multiple approaches, this new method is efficient, widely applicable, and highly reproducible. The identified policy hotspots and foci reveal core priorities and support informed decision-making, enhancing both efficiency and effectiveness for policy analysis and decision.

关键词：Policy foci; Policy hotspots; Co-word network; H-type metrics; Generative artificial intelligence; Gene editing

发表时间：2026-06

DOI：https://doi.org/10.1016/j.joi.2026.101815

收录数据库：Elsevier ScienceDirect