基于特征分析的政府数据分类分级政策量化评价

Quantitative Evaluation of Government Data Classification and Grading Policies Based on Feature Analysis

[目的/意义]数字化改革的提出,让数据分类分级的发展走上了“快车道”。近几年,各地政府先后出台多份有关政策来落实。但是,政策的合理性、政策与行业数据的匹配程度等还有待讨论。[方法/过程]文章采用描述性分析、共现词分析、聚类分析方法对2016-2021年间各地政府发布的54份数据分类分级政策文本进行研究,并在此基础上构建数据分类分级政策的评价框架,根据一定的规则选取8份具有代表性的政策样本,利用PMC-NMF模型进行量化。[结果/结论]分类分级政策发展速度较快,政策内容也结合了市场行业的实际情况进行制定,较为合理,但在丰富政策内容、强调政策工具的搭配使用、扩大政策适用对象的范围、标准化文件的引用等方面需要进一步完善。

[Purpose/significance]The proposal of digital reform has put the development of data classification on the"fast track".In recent years,governments around the world have issued a number of relevant policies for implementation.However,the rationality of the policies and the degree of matching between the policies and industry data have yet to be discussed.[Method/process]This paper adopts descriptive analysis,co-occurrence word analysis and cluster analysis methods to study 54 data classification and grading policy texts issued by governments around the world during the period of 2016-2021,and constructs an evaluation framework for data classification and grading policies on this basis,and selects 8 representative policy samples according to certain rules,and quantifies them by using PMC-NMF model.[Result/conclusion]The classification and grading policy has developed at a faster pace,and the policy content has been formulated in the light of the actual situation of the market sector,which is more reasonable.However,further improvements are needed in terms of enriching the policy content,emphasising the matching use of policy tools,expanding the scope of the policy applicability targets,and citing standardised documents,etc.