Centroid margin, an embedding-based measure of relative domain distinctiveness, was evaluated as a pre-data item-selection tool for multi-domain instruments. Across four samples, centroid-guided selection improved model fit and discriminant validity, whereas own-domain cosine similarity increased overlap among closely related domains, revealing a key limitation of absolute similarity metrics.