Text embeddings are widely used to analyse large corpora of complex texts. However, it is unclear whether the embeddings capture the same semantic distances as the human experts using them. Ensuring alignment between embedding representations and human intentions is essential for valid analyses. We present the Stakeholder Grounding Exercise, a method for making expert associations explicit and grounding embedding model results in human understanding. In our primary case study on Danish policy issues, we find that neural text embeddings are substantially less reliable than human experts (19-26 pp gap), and that this misalignment propagates to downstream clustering performance (Spearman $ρ=0.9$ between exercise ranking and cluster quality). A secondary study on US Federal AI use cases replicates the gap (16pp) in English, using a digital protocol and a different community of experts -- demonstrating that the gap is not an artefact of a single instrument or domain. The Stakeholder Grounding Exercise offers a practical method for assessing whether embedding models capture the semantic distinctions that matter most to domain experts.

翻译：文本嵌入被广泛用于分析复杂文本的大规模语料库。然而，尚不明确这些嵌入是否捕捉到与使用它们的领域专家相同的语义距离。确保嵌入表征与人类意图的一致性对于有效分析至关重要。我们提出利益相关者锚定实验（Stakeholder Grounding Exercise），一种将专家关联显式化并将嵌入模型结果锚定于人类理解的方法。在对丹麦政策议题的主要案例研究中，我们发现神经文本嵌入的可靠性显著低于人类专家（差距19-26个百分点），且这种偏差会传递到下游聚类性能（实验排名与聚类质量之间的斯皮尔曼相关系数ρ=0.9）。针对美国联邦AI用例的辅助研究使用数字协议和不同专家群体，在英语环境下复现了该差距（16个百分点），表明该差距并非单一工具或领域的伪迹。利益相关者锚定实验提供了一种实用方法，用于评估嵌入模型是否捕捉到对领域专家最为关键的语义区分。