题目：Credibility and Use of Scientific and Technical Information in Policy Making
主讲人：Jan Youtie副教授（佐治亚理工学院）
时间：2015年5月13日14:00--16:00
地点：主楼418
主讲人简介：
    Jan Youtie博士是佐治亚理工学院经济发展研究中心首席研究员，公共政策学院副教授，主要研究方向是新兴技术评估、创新和知识的测量评估、基于科技的经济发展、制造业竞争力。她是多个国际期刊的专门审稿人，先后主持和参与项目30余项，出版专著10本，发表学术论文30余篇，其文章“协调工业现代化服务：美国制造业拓展合伙关系的影响和分析”曾获得美国Lang Rosen优秀论文金奖。
内容简介：
    We use text mining methods to analyze field structured data in journal publications and patents. But are these publications used in innovation policymaking? And how can we use our text mining methods to find this out? Many studies of the use of information in policymaking have been performed, but almost none look at the use of scientific and technical information and of these, most are based on anecdotes rather than quantitative information. This National Science Foundation project seeks to develop a new quantitative approach to examine the use of scientific and technical information (primarily journal articles) in reports of the National Academy of Science. The National Academy of Science serves as the “science advisor to the US Congress” so it could be argued that if scientific and technical information would be used anywhere, it would be used in National Academy of Science reports. In this project, we code the characteristics of these reports, committee members involved in writing the reports, cited references that are partially comprised of scientific and technical information, political system characteristics, and whether or not the National Academy of Science report is actually used by Congress. The results show that scientific and technical information is widely used in these reports, with use varying by policy area, year and size of the report, and sectoral affiliation of the authors of the report. However, reports with a substantial share of scientific and technical information are less likely to be used by Congress. In this seminar we will discuss the novel method used to obtain these findings and the benefits, as well as challenges and drawbacks of applying this text mining method to policy documents.


（主办：管理工程系）