Quantitative evaluation of China’s private universities provincial public funding policies based on the PMC-Index model

Abstract

The special public funding policies, formulated and implemented by provincial governments, plays an important role in the development of private universities in China. However, there is a lack of scientific evaluation on the rationality and completeness of the provincial special public funding policy of China’s private colleges and universities. Therefore, this paper uses PMC-index model and text mining technology to establish an evaluation index system for the provincial special public funding policy of private universities in China. Based on PMC-Index and PMC-Surface, 13 policy texts issued since 2010 were evaluated scientifically. The results show that the average PMC-Index of the 13 policies is 6.97, and the PMC-Surface map is smooth, which indicates that the overall structure of these policies is reasonable and the policy content has certain completeness. Only one policy is unacceptable. Through further analysis, we found that there is still much room for improvement in the content design of the sample policy, and its rationality and completeness are related to the publication time. This study is helpful to fully understand the advantages and disadvantages of the provincial special public funding policy of private universities in China.

Research on quantitative evaluation of digital economy policy in China based on the PMC index model

“…These 10 primary variables are as follows: Policy Type (X 1 ), Policy Effectiveness (X 2 ), Policy Level (X 3 ), Policy Felids (X 4 ), Policy Guarantee (X 5 ), Policy Audience (X 6 ), Policy Objectives (X 7 ), Policy Evaluation (X 8 ), Policy Perspective (X 9 ), and Policy Publicity (X 10 ). Concurrently, through text mining and considering the current state of digital economic development, and drawing insights from the research of scholars Kuang [ 32 ], Liu [ 33 ], Hu [ 34 ] and Yang [ 35 ], the Chinese digital economic policy PMC model variables were formulated. This encompasses 10 primary variables and 45 secondary variables, and the evaluation criteria are in binary form, with detailed outcomes presented in Table 3 .…”

“…and considering the current state of digital economic development, and drawing insights from the research of scholars Kuang [32], Liu [33], Hu [34] and Yang [35], the Chinese digital economic policy PMC model variables were formulated. This encompasses 10 primary variables and 45 secondary variables, and the evaluation criteria are in binary form, with detailed outcomes presented in Table 3.…”

Quantitative evaluation of science and technology financial policies based on the PMC-AE index model: A case study of China’s science and technology financial policies since the 13th five-year plan

“…This method judges the consistency of policies from a multidimensional perspective and directly observes the strengths and weaknesses of policy texts by constructing the concave and convex shapes of the PMC surface [ 44 , 45 ]. Since then, this method has been widely applied to policy evaluation research: for example, in the construction industry [ 46 ]; nursing insurance policies [ 47 ] and farmland protection policies [ 48 ]; China’s pork industry [ 49 ]; the efficiency of green development in the Yangtze River Economic Belt [ 50 ]; China’s watershed ecological compensation policies [ 51 ] and the effectiveness evaluation of provincial public funding policies for private colleges in China [ 52 ]. The PMC method has been used to study the effectiveness of related policies, providing a good evaluation of the consistency and deficiencies of different policies during the specific implementation process.…”