对比美欧韩三国AI政策研究预印本使用趋势,发现疫情与ChatGPT加速了开放科学传播。
The Shift Towards Preprints in AI Policy Research: A Comparative Study of Preprint Trends in the U.S., Europe, and South Korea
- 通过文献计量分析2015–2024年美欧韩三国预印本引用变化,识别重大事件影响。
- 美国预印本引用呈事件驱动式激增,欧洲为制度推动型增长,韩国保持稳定线性上升。
- 揭示本地科研文化与开放科学成熟度对预印本采纳路径的决定性影响,适合政策制定者参考。
开放科学的兴起正在重塑全球人工智能(AI)政策研究的传播方式。本研究考察了美国、欧洲和韩国在2015至2024年间预印本引用的区域趋势,重点关注新冠疫情和ChatGPT发布这两个重大事件对研究传播模式的影响。基于Web of Science的文献计量数据,研究追踪了全球突发事件如何影响各地区在AI政策研究中对预印本的采用,并通过标记事件时间点分析其影响。结果显示,尽管所有地区均出现预印本引用增长,但增长幅度与轨迹显著不同:美国呈现明显事件驱动的快速上升;欧洲表现为制度推动下的持续增长;韩国则维持稳定线性增长。这些发现表明,全球突发事件虽可能加速预印本采纳,但具体程度与路径受本地科研文化、政策环境及开放科学成熟度制约。本文强调未来AI治理策略需考虑研究传播的区域性差异,并提出开展纵向与比较研究以深化对开放获取采纳机制的理解。
原文摘要 · Abstract (English)
The adoption of open science has quickly changed how artificial intelligence (AI) policy research is distributed globally. This study examines the regional trends in the citation of preprints, specifically focusing on the impact of two major disruptive events: the COVID-19 pandemic and the release of ChatGPT, on research dissemination patterns in the United States, Europe, and South Korea from 2015 to 2024. Using bibliometrics data from the Web of Science, this study tracks how global disruptive events influenced the adoption of preprints in AI policy research and how such shifts vary by region. By marking the timing of these disruptive events, the analysis reveals that while all regions experienced growth in preprint citations, the magnitude and trajectory of change varied significantly. The United States exhibited sharp, event-driven increases; Europe demonstrated institutional growth; and South Korea maintained consistent, linear growth in preprint adoption. These findings suggest that global disruptions may have accelerated preprint adoption, but the extent and trajectory are shaped by local research cultures, policy environments, and levels of open science maturity. This paper emphasizes the need for future AI governance strategies to consider regional variability in research dissemination and highlights opportunities for further longitudinal and comparative research to deepen our understanding of open-access adoption in AI policy development.
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