用预训练的语言模型揭示气候研究在政策中的驱动因素
Basil Mahfouz1, Licia Capra2, Geoff Mulgan1
1UCL Department of Science, Technology, Engineering and Public Policy, London, UK.
Patterns (New York, N.Y.)
|December 2, 2025
概括
学术引用和媒体提及强烈预测研究对气候变化政策的影响. 本研究使用先进的语言模型来识别影响研究采用的因素,控制政策相关性.
科学领域:
- 环境科学 环境科学
- 政策研究 政策研究
- 计算社会科学 计算社会科学
背景情况:
- 基于证据的政策制定对于社会挑战至关重要.
- 在政策中理解研究吸收是不清楚的.
- 之前的研究忽视了政策相关性,可能会对影响力驱动器的结论产生偏见.
研究的目的:
- 确定推动研究被纳入政策的因素.
- 在分析研究影响时控制政策相关性.
- 用气候变化作为政策采用的案例研究.
主要方法:
- 利用预训练的语言模型来识别语义上相似的研究论文对.
- 在政策中引用的论文与未引用的论文进行了比较,以控制政策相关性.
- 开发了一种计算方法来隔离对政策引用概率的影响.
主要成果:
- 学术引用是对气候变化政策影响的最强有力的预测指标.
- 媒体提及是政策影响的第二强预测因素.
- 计算方法有效地控制了政策相关性.
结论:
- 学术引用和媒体的存在显著地影响了气候变化研究在政策中的采用.
- 开发的计算方法可以应用于其他科学领域进行比较分析.
- 这种方法提高了对研究转化为政策的转化机制的理解.
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