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美国公众对美国国家科学院共识报告的广泛使用。

Widespread use of National Academies consensus reports by the American public.

机构信息

School of Public Policy, Georgia Institute of Technology, Atlanta, GA 30332;

School of Public Policy, Georgia Institute of Technology, Atlanta, GA 30332.

出版信息

Proc Natl Acad Sci U S A. 2022 Mar 1;119(9). doi: 10.1073/pnas.2107760119.

DOI:10.1073/pnas.2107760119
PMID:35193972
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8892306/
Abstract

In seeking to understand how to protect the public information sphere from corruption, researchers understandably focus on dysfunction. However, parts of the public information ecosystem function very well, and understanding this as well will help in protecting and developing existing strengths. Here, we address this gap, focusing on public engagement with high-quality science-based information, consensus reports of the National Academies of Science, Engineering, and Medicine (NASEM). Attending to public use is important to justify public investment in producing and making freely available high-quality, scientifically based reports. We deploy Bidirectional Encoder Representations from Transformers (BERT), a high-performing, supervised machine learning model, to classify 1.6 million comments left by US downloaders of National Academies reports responding to a prompt asking how they intended to use the report. The results provide detailed, nationwide evidence of how the public uses open access scientifically based information. We find half of reported use to be academic-research, teaching, or studying. The other half reveals adults across the country seeking the highest-quality information to improve how they do their job, to help family members, to satisfy their curiosity, and to learn. Our results establish the existence of demand for high-quality information by the public and that such knowledge is widely deployed to improve provision of services. Knowing the importance of such information, policy makers can be encouraged to protect it.

摘要

在寻求理解如何保护公共信息领域免受腐败影响时,研究人员理所当然地关注功能失调问题。然而,公共信息生态系统的某些部分运作得非常好,了解这一点也将有助于保护和发展现有优势。在这里,我们关注这一差距,重点关注公众对高质量基于科学的信息的参与,以及美国国家科学院、工程院和医学院(NASEM)的共识报告。关注公众的使用对于证明公共投资于生产和免费提供高质量、基于科学的报告是合理的。我们使用双向编码器表示转换器(BERT),一种高性能、监督机器学习模型,对 160 万条由美国下载者留下的评论进行分类,这些评论是对一个提示的回应,询问他们打算如何使用该报告。结果提供了详细的、全国性的证据,说明公众如何使用开放获取的基于科学的信息。我们发现,一半的报告用途是学术研究、教学或学习。另一半则揭示了全国各地的成年人都在寻求最高质量的信息,以提高他们的工作效率,帮助家庭成员,满足他们的好奇心,以及学习。我们的研究结果表明,公众对高质量信息存在需求,而且这种知识被广泛用于改善服务提供。了解此类信息的重要性,可以鼓励政策制定者加以保护。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf12/8892306/82df486e4e45/pnas.2107760119fig01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf12/8892306/82df486e4e45/pnas.2107760119fig01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf12/8892306/82df486e4e45/pnas.2107760119fig01.jpg

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A characterization of professional media and its links to research.专业媒体的特征及其与研究的联系。
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5
Lay information mediary behavior uncovered: exploring how nonprofessionals seek health information for themselves and others online.揭示大众信息中介行为:探索非专业人士如何在网上为自己和他人寻求健康信息。
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