我国政策文本分析的核心议题与分析方法
Think Tank of Scinece&Technology(2023)
华中科技大学
Abstract
政策文本分析是发掘某个时代意识形态的生产、流动以及社会权力关系的重要手段,通过挖掘政策文本分析领域的发展现状和热点问题,可为今后政策文本分析提供有益参考.文章选取2003—2022年CSSCI来源的810篇相关文献,借助文献可视化分析软件CiteSpace绘制国内政策文本分析的科学知识图谱,进而梳理出政策文本分析的核心议题和分析方法.研究表明,政策文本分析的研究经历了对政策文件的主观解读、对政策特征的客观分析、分析政策发展规律以及呈现政策主体互动关系4个阶段,每个研究阶段均在核心议题和分析方法上呈现出各自的特点.由于政策文本数据的井喷式增加,政策文本分析的相关研究未来仍需关注政策遵从、个体的政策执行行为以及政策制定三个核心议题,并逐渐丰富有关政策文本的因果检验.
MoreTranslated text
Key words
Policy text analysis,Core topic,Analysis methods,CiteSpace
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined