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Description: Taxonomic incommensurability denotes the difficulty in comparing scientific theories due to incompatible use of concepts and operationalizations. We show that item, scale, and label embeddings---representations of psychometric items, scales, and labels in a vector space obtained from language models---can help tackle this problem in psychology. We analyze over 4,000 items, 450 scales, and 270 different construct labels to show that embeddings can be used to predict empirical intercorrelations between operationalizations, perform automated detection of jingle--jangle fallacies, and suggest more parsimonious taxonomies that might eliminate a number of extant psychological constructs. All in all, our work suggests that embeddings offer a useful tool to tackle taxonomic incommensurability in the sciences.