The Prediction-Measurement Gap: Toward Meaning Representations as Scientific Instruments

ArXi:2603.10130v1 Announce Type: new Text embeddings have become central to computational social science and psychology, enabling scalable measurement of meaning and mixed-method inference. Yet most representation learning is optimized and evaluated for prediction and retrieval, yielding a prediction-measurement gap: representations that perform well as features may be poorly suited as scientific instruments.