情报过程视角下大数据政策量化评价——以11项国家级大数据政策为例  

Quantitative Evaluation of Big Data Policies from the Perspective of Information Process:Taking 11 National Big Data Policies as an Example

作　　者：胡峰 温志强[3] 沈瑾秋[1,2] 姚缘[4] Hu Feng;Wen Zhiqiang;Shen Jinqiu;Yao Yuan(Jiangsu Academy of Science and Technology for Development,Nanjing 210042,China;Jiangsu Information Institute of Science and Technology,Nanjing 210042,China;College of Politics and Public Administration,Tianjin Normal University,Tianjin 300387,China;College of Cultural Industries,Nanjing University of the Arts,Nanjing 210013,China)

机构地区：[1]江苏省科学技术发展战略研究院,江苏南京210042 [2]江苏省科学技术情报研究所,江苏南京210042 [3]天津师范大学政治与行政学院,天津300387 [4]南京艺术学院文化产业学院,江苏南京210013

出　　处：《中国科技论坛》2020年第4期30-41,73,共13页Forum on Science and Technology in China

摘　　要：本文以2015—2019年中央国家机关发布的11项大数据政策为样本,以Herring模型为研究框架,从情报需求识别、规划、搜集、加工、分析、演示的过程视角对大数据政策文本进行量化评价。基于PMC指数模型,计算11项大数据政策的PMC指数值和凹陷程度,具体评价每项大数据政策的优劣,并提出参考性的政策改进路径。结果显示,大数据政策设计总体较为科学、合理,政策质量较高,其中2项政策等级为完美、7项为优秀、2项为可接受,但在政策时效、政策工具、政策受众和政策内容4个指标上得分较低,未来需要在这4个方面加以改进。This paper took 11 big data policies issued by central government departments from 2015 to 2019 as samples,and took Herring model as the research framework.It evaluated the big data policies quantitatively from the perspective of the process of identification,planning,collection,processing,analysis and demonstration of information needs.Based on the PMC index model,the PMC index value and depression degree of 11 big data policies were calculated,and the merits and demerits of each big data policy were evaluated concretely,and the referential policy improvement path were put forward.The result shows that the overall design of big data policies is scientific and reasonable,and the quality of policies is proven to be high.Among these policies,two policies level are perfect,seven are excellent,and two are acceptable.However,the scores on the four factors of policy timeliness,policy tools,policy audience and policy content are low,which requires improvement in the future.

关 键 词：PMC指数 Herring模型 大数据政策 量化评价 

分 类 号：F426[经济管理—产业经济]