基于PMC指数模型的中国数字农业政策定量评价与优化

Quantitative Evaluation and Optimization of China's Digital Agriculture Policy Based on the PMC Index Model

摘要:

数字农业政策是实现农业农村现代化和推进数字农业建设的重要保障。本文首先采用内容分析法对25项数字农业政策进行梳理,借助ROSTCM6.0提取核心关键词,绘制语义网络图,构建PMC指数模型,对10项样本政策进行量化评价。结果发现10项样本政策的PMC指数均值是6.48,1项优秀、6项良好、3项及格,说明政策整体水平设计较为科学合理,但也存在政策内容不完善、时效过长、视角单一的问题。本文创新性的构建了“主体-领域-内容-评价-效力”的五维评价框架,建立了数字农业政策评价指标,并利用定量方法从政策性质、政策时效、政策视角对10项样本政策提出优化建议,提高了政策评价的客观性和准确性。

Abstract:

The digital agriculture policy is an important guarantee for realizing the modernization of agriculture and rural areas and promoting the construction of digital agriculture. In this paper, 25 digital agriculture policies were sorted out by using the content analysis method, the core Keywords were extracted with the help of ROSTCM6.0, the semantic network diagram was drawn, the PMC index model was constructed, and the 10 sample policies were quantitatively evaluated. The results show that the average PMC index of the 10 sample policies is 6.48, with 1 excellent, 6 good, and 3 passing, indicating that the overall level design of the policy is relatively scientific and reasonable, but there are also problems such as imperfect policy content, too long timeliness, and a single perspective. This paper innovatively constructs a five-dimensional evaluation framework of "subject-field-content-evaluation-effectiveness", establishes the evaluation index of digital agriculture policy, and uses quantitative methods to put forward optimization suggestions for 10 sample policies from the perspective of policy nature, policy timeliness and policy, so as to improve the objectivity and accuracy of policy evaluation.