通过文本为数据方法理解政策设计:政策设计注释 (POLIANNA) 数据集
Sebastian Sewerin1, Lynn H Kaack2,3,4, Joel Küttel5,6
1Institute for Environment and Sustainability (IES), Lee Kuan Yew School of Public Policy, National University of Singapore, Singapore, Singapore. sebastian.sewerin@gess.ethz.ch.
Scientific data
|December 13, 2023
概括
一个新的数据集,POLIANNA,提供了欧盟气候和能源政策的注释. 该资源使机器学习能够进行可扩展的政策分析,克服手动文本评估的局限性.
科学领域:
- 环境政策分析 环境政策分析
- 计算社会科学 计算社会科学
- 机器学习应用 机器学习应用
背景情况:
- 手动分析气候和能源政策是耗时和昂贵的.
- 对政策设计的系统评估对于有效减缓气候变化至关重要.
- 现有的方法很难扩大对广泛的政策文件分析的规模.
研究的目的:
- 介绍POLIANNA,这是一个新的数据集,包含欧盟 (EU) 气候和能源政策文本的注释.
- 促进用于自动化政策分析的监督机器学习模型的开发.
- 解决政策研究中手动文本分析的局限性.
主要方法:
- 开发一种基于理论政策设计概念的新型编码方案.
- 20,577个文本的注释涵盖了18个欧盟气候变化减缓和可再生能源政策.
- 对注释者之间的协议进行分析,以确保数据可靠性.
主要成果:
- 创建了POLIANNA数据集,这是政策信息学的宝贵资源.
- 展示一种将政策设计分类学转化为文本注释的方法.
- 建立一个开发人工智能驱动的政策分析工具的基础.
结论:
- 波利安纳数据集允许对气候和能源政策进行可扩展,数据驱动的分析.
- 自动化工具可以通过建议相关的文本段进行手动审查来帮助研究人员.
- 这项工作推动了气候行动计算政策分析领域的发展.
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