Quantitative Evaluation of Government Data Classification and Grading Policies Based on Feature Analysis
[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 implementa-tion.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 ba-sis,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,ex-panding the scope of the policy applicability targets,and citing standardised documents,etc.
classification and gradingpolicy evaluationPMC-NMF model
万方数据
维普
