气候和环境框架实验的系统映射以及利用计算方法进行的重新分析表明存在遗漏的相互作用偏差

L. Fesenfeld, Liam F. Beiser‐McGrath, Yixian Sun, M. Wicki, Thomas Bernauer
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引用次数: 0

摘要

雄心勃勃的气候政策需要得到数百万人的认可,他们的日常生活将受到代价高昂的影响。反过来,这就需要了解如何让大众接受并防止对代价高昂的气候政策产生政治反弹。许多学者认为,"框架"--向特定人群强调特定政治论点的特别定制信息--是改变气候信仰、态度和行为的有效传播策略。与此相反,另一些学者则认为,人们持有相对稳定的观点,因此怀疑 "框架 "能否改变公众对气候变化等突出问题的看法。我们从两个方面对这一争论做出了贡献:首先,我们对发表在 46 份同行评审期刊上的 121 项关于气候和环境政策框架的实验研究进行了系统的梳理,并展示了对这些研究的作者进行调查的结果。其次,我们说明了如何使用新颖的计算方法来检查亚组效应的稳健性并识别遗漏的交互偏差。我们发现,大多数实验报告了显著的主效应和亚组效应,但很少使用先进的方法来解释潜在的遗漏交互作用偏差。此外,只有少数研究公开了数据,以便于复制。我们对框架研究人员的调查表明,当学者们成功发表非显著效应时,这些效应通常与其他显著效应捆绑在一起,以增加发表的机会。最后,我们利用贝叶斯计算稀疏回归技术,对 10 项研究进行了说明性的重新分析,重点研究了按党派划分的亚组框架差异(气候变化态度的一个关键驱动因素),结果表明,当考虑到遗漏的交互偏差时,这些效应往往并不稳健。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Systematic mapping of climate and environmental framing experiments and re-analysis with computational methods points to omitted interaction bias
Ambitious climate policy requires acceptance by millions of people whose daily lives would be affected in costly ways. In turn, this requires an understanding of how to get the mass public on board and prevent a political backlash against costly climate policies. Many scholars regard ‘framing’, specially tailored messages emphasizing specific subsets of political arguments to certain population subgroups, as an effective communication strategy for changing climate beliefs, attitudes, and behaviors. In contrast, other scholars argue that people hold relatively stable opinions and doubt that framing can alter public opinion on salient issues like climate change. We contribute to this debate in two ways: First, we conduct a systematic mapping of 121 experimental studies on climate and environmental policy framing, published in 46 peer-reviewed journals and present results of a survey with authors of these studies. Second, we illustrate the use of novel computational methods to check for the robustness of subgroup effects and identify omitted interaction bias. We find that most experiments report significant main and subgroup effects but rarely use advanced methods to account for potential omitted interaction bias. Moreover, only a few studies make their data publicly available to easily replicate them. Our survey of framing researchers suggests that when scholars successfully publish non-significant effects, these were typically bundled together with other, significant effects to increase publication chances. Finally, using a Bayesian computational sparse regression technique, we offer an illustrative re-analysis of 10 studies focusing on subgroup framing differences by partisanship (a key driver of climate change attitudes) and show that these effects are often not robust when accounting for omitted interaction bias.
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