We use GPT-4 to obtain position estimates of political texts in continuous spaces. We develop and validate a new approach by positioning British party manifestos on the economic, social, and immigration policy dimensions and tweets by members of the US Congress on the left-right ideological spectrum. For the party manifestos, the correlation between the positions produced by GPT-4 and experts is 93% or higher, a performance similar to or better than that obtained with crowdsourced position estimates. For individual tweets, the positions obtained with GPT-4 achieve a correlation of 91% with crowdsourced position estimates. For senators of the 117th US Congress, the positions obtained with GPT-4 achieve a correlation of 97% with estimates based on roll call votes and of 96% with those based on campaign funding. Correlations are also substantial within party, indicating that position estimates produced with GPT-4 capture within-party differences between senators. Overall, using GPT-4 for ideological scaling is fast, cost-efficient, and reliable. This approach provides a viable alternative to scaling by both expert raters and crowdsourcing.

翻译：我们采用 GPT-4 对政治文本在连续空间中的立场估计进行提取。通过将英国政党宣言置于经济、社会和移民政策维度，以及将美国国会议员的推文置于左右意识形态光谱上，我们开发并验证了一种新方法。对于政党宣言，GPT-4 生成的立场与专家评估的相关性达到 93% 或更高，其性能与通过众包获得的立场估计相当或更优。对于个别推文，GPT-4 获取的立场与众包立场估计的相关性为 91%。对于第 117 届美国国会的参议员，GPT-4 获取的立场与基于唱名投票的估计相关性达 97%，与基于竞选资金的估计相关性达 96%。即使在党内，相关性也相当显著，表明 GPT-4 产生的立场估计能够捕捉到参议员之间的党内差异。总体而言，使用 GPT-4 进行意识形态量化分析具有快速、成本效益高且可靠的特点。该方法为专家评分和众包量化分析提供了一种可行的替代方案。