摘要
近年来,中央及地方政府先后出台了一系列的减负政策,政策的科学性与合理性如何值得探究。文章基于内容分析法,以2018—2022年我国12个省市发布的36份减负政策文本为样本,进行统计分析。研究发现:政策工具的应用存在结构差异,政策使用呈现偏斜特征;政策工具的选用注重短期效应,忽视长期性效果的发挥;政策工具使用缺乏整合性,融合度较差;各个省市减负政策存在同质化倾向,地域独特性不足。我们可以从优化减负政策工具及措施的使用结构、兼顾政策短期效益和长期政策目标、努力打出减负政策的“组合拳”和增强减负政策的“本土化”特征四个方面综合施策,凸显义务教育减负工作政策指引的精准性和针对性。
In recent years,the central and local governments have issued a series of policies to reduce the burden. How to explore its science and rationality is worth exploring. Based on the content analysis method,36 burden reduction policy texts released by 12 provinces and cities in China from 2018 to 2022 were used as samples for statistical analysis. The findings are as follows:there are structural differences in the application of policy instruments,and the use of policy is skewed. The choice of policy instruments pays attention to the short-term effect,ignoring the long-term effect;lack of integration in the use of policy tools and poor degree of integration;the burden reduction policies of each province and city have the tendency of homogeneity and lack of regional uniqueness. Based on this,the policy should be comprehensively implemented from four aspects:optimizing the use structure of the policy tools and measures,taking into account the shortterm benefits and long-term policy objectives of the policy,making efforts to play a“combination punch”of the burden reduction policy,and strengthening the“localization”characteristics of the burden reduction policy,to highlight the accuracy and pertinence of the policy guidance of the burden reduction work in compulsory education.
作者
王颖碟
马宇祥
王标
WANG Yingdie;MA Yuxiang;WANG Biao(School of Education,Hainan Normal University,Haikou Hainan 571158;School of Public Administration,Shanxi Agricultural University,Jinzhong Shanxi 030801;Dean’s office,Hainan Normal University,Haikou Hainan 571158,China)
出处
《教育参考》
2022年第6期37-46,共10页
Education Approach
基金
2022年度海南师范大学研究生创新科研课题“政策工具视角下教师教育振兴政策量化分析及优化策略”(课题编号:hsyx2022-28)的研究成果之一。
关键词
政策工具
减负
量化研究
政策优化
Policy Tools
Burden Reduction
Quantitative Analysis
The Policy Optimization