AI 中文总结
本文提出基于多维意识形态嵌入的选票转移模型，校准于2017和2022年法国选举，在2024年复杂三阵营竞争中达90.34%选区准确率，证明意识形态邻近性主导转移且左右轴不足，并揭示极右翼实际劣势。
AI 中文摘要
两轮立法选举的结果不仅取决于第一轮的得票份额，还取决于其首选政党未能晋级的选民如何在幸存的候选人之间重新分配选票。我们开发了一个基于原则的选票转移过程模型，该模型植根于从Chapel Hill专家调查中得出的多维意识形态嵌入，并将其应用于法国立法选举。该模型在2017年和2022年的选举上进行了校准，并在异常复杂的2024年选举中进行了评估，此次选举中三个意识形态迥异的阵营同时进入第二轮，模型实现了90.34%的选区级准确率。我们表明，仅意识形态 proximity 就能解释绝大多数选票转移，简单的左右轴不足以捕捉相关距离，并且2024年选民的结构配置对极右翼的有利程度远低于选前预测所暗示的。
英文摘要
Two-round legislative elections are decided not only by first-round vote shares, but by how voters whose preferred party did not advance redistribute their votes among the surviving candidates. We develop a principled model of this transfer process, grounded in multi-dimensional ideological embeddings derived from the Chapel Hill Expert Survey, and apply it to French legislative elections. Calibrated on the 2017 and 2022 elections, the model is evaluated on the unusually complex 2024 contest, in which three ideologically distinct blocs reached the second round simultaneously, achieving 90.34 percent constituency-level accuracy. We show that ideological proximity alone explains the large majority of vote transfers, that a simple left-right axis is insufficient to capture the relevant distances, and that the structural configuration of the 2024 electorate was far less favorable to the far right than pre-election forecasts suggested.
