量化美国政策文件中引用的青年研究的相关性
Quantifying the Relevance of Youth Research Cited in the US Policy Documents
AI总结：
本文利用自然语言处理、预训练大语言模型和统计分析，量化美国政策文件中引用的青年研究的相关性，发现获得政策引用的青年相关研究大多与引用政策文件相关。
摘要：
近年来，人们越来越关注并强调在学术或科学研究共同体之外开展研究，以造福整个社会。衡量研究对社会影响的一种众所周知的方法是统计其政策引用次数。尽管研究在为政策提供信息方面十分重要，但目前没有具体证据表明被引研究在引用它的政策文件中具有相关性。这令人担忧，因为这可能增加政策中所用证据被个人、社会或政治偏见操纵的可能性，从而导致政策中出现不适当、碎片化或过时的研究证据。因此，识别研究文章与引用它的政策文件之间的相关程度至关重要。在本文中，我们利用自然语言处理技术、最先进的预训练大语言模型（LLMs）以及统计分析，考察了以青年为重点的研究在被引用的美国政策文件中的上下文相关性规模。我们的实验和分析得出结论：获得美国政策引用的青年相关研究文章大多与引用它们的政策文件相关。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
