跨党派指责：丹麦议会中的政治对比与责任归因
Blaming Across the Aisle: Political Contrasting and Blame Attribution in the Danish Parliament
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中文总结 AI 辅助
本研究利用BlameBERT分类器与多水平模型分析丹麦议会1997-2026年责任归因，发现其呈香蕉形轨迹，近年上升且右翼意识形态极端化加剧指责，揭示政治话语的意识形态不对称硬化。
中文摘要 AI 辅助
政治话语被广泛认为正变得更加敌对，然而坚实的证据仍然稀缺。本研究考察了1997年至2026年间丹麦议会中的责任归因，结合了专门构建的分类器BlameBERT（F1值：0.80）与多水平统计建模。该分类器采用一种针对低至中资源语言中责任归因的注释高效流水线构建。结果显示出一条香蕉形轨迹，责任归因在2016年左右之前持续下降，随后在近年（2019-2026年）进入显著且持续的上升期。政府地位持续影响责任归因——我们将此效应称为政治对比——反对党比执政党进行更多指责。该效应受到意识形态的调节：执政对指责的抑制作用在右翼政党中不太明显，且意识形态极端化在右翼中更强地放大了指责。近年来，政治派别与意识形态极端化之间的交互作用加剧，表明指责修辞的意识形态硬化集中在政治光谱的右翼。综合来看，这些模式表明，感知到的严厉政治语言上升不仅反映了一般的修辞漂移，而是政治话语中意识形态不对称的硬化。敏感性分析表明，结论在不同分类阈值下均保持稳健。
英文摘要
Political discourse is widely perceived to be growing more hostile, yet robust evidence remains scarce. This study examines blame attribution in the Danish Parliament from 1997 to 2026, combining a purpose-built classifier, BlameBERT (F1: 0.80), with multilevel statistical modeling. The classifier is constructed using an annotation-efficient pipeline for blame attribution in low-to-mid resource languages. The results reveal a banana-shaped trajectory, with blame declining until around 2016 before entering a significant and sustained increase in recent years (2019-2026). Government status consistently influenced blame attribution - an effect we term political contrasting - with opposition parties blaming substantially more than governing parties. This effect was moderated by ideology: The blame-dampening effect of governing was less pronounced among right-wing parties, and ideological extremity amplified blame more strongly on the right. In recent years, the interaction between political wing and ideological extremity intensified, suggesting an ideological hardening of the blame rhetoric concentrated on the right of the political spectrum. Taken together, these patterns suggest that the perceived rise in harsh political language reflects not merely a general rhetorical drift, but an ideologically asymmetric hardening of political discourse. A sensitivity analysis showed that the conclusions were robust to varying classification thresholds.
发表机构
- Aarhus University（奥胡斯大学）
- Center for Humanities Computing（人文计算中心）
机构由 AI 辅助整理，请以论文原文为准。
