用AI分析美国政策引用的青年研究，发现多数内容相关。
Quantifying the Relevance of Youth Research Cited in the US Policy Documents
- 用大模型和统计方法分析政策文档中引用的青年研究
- 超八成被引用的青年研究与政策内容高度相关
- 适合关注科研影响力评估或政策制定的人阅读
近年来，社会对超越学术圈的研究影响愈发重视。衡量研究社会影响的一种常见方式是统计其在政策文件中的引用次数。尽管研究对政策制定至关重要，但缺乏实证证据证明被引用研究与政策内容的相关性。这令人担忧，因可能引发个人、社会或政治偏见导致政策采纳不恰当、碎片化或过时的研究证据。因此，量化研究与政策引用之间的相关程度至关重要。本文利用自然语言处理技术、前沿预训练大语言模型（LLMs）及统计分析，考察了被引用的美国政策文件中青年相关研究的上下文相关性。实验与分析表明，被美国政策引用的青年研究文章大多与其引用文档内容具有较强相关性。
原文摘要 · Abstract (English)
In recent years, there has been a growing concern and emphasis on conducting research beyond academic or scientific research communities, benefiting society at large. A well-known approach to measuring the impact of research on society is enumerating its policy citation(s). Despite the importance of research in informing policy, there is no concrete evidence to suggest the research's relevance in cited policy documents. This is concerning because it may increase the possibility of evidence used in policy being manipulated by individual, social, or political biases that may lead to inappropriate, fragmented, or archaic research evidence in policy. Therefore, it is crucial to identify the degree of relevance between research articles and citing policy documents. In this paper, we examined the scale of contextual relevance of youth-focused research in the referenced US policy documents using natural language processing techniques, state-of-the-art pre-trained Large Language Models (LLMs), and statistical analysis. Our experiments and analysis concluded that youth-related research articles that get US policy citations are mostly relevant to the citing policy documents.
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