The Prediction-Measurement Gap: Toward Meaning Representations as Scientific Instruments
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. The paper argues that scientific meaning analysis motivates a distinct family of objectives - scientific usability - emphasizing geometric legibility, interpretability and traceability to linguistic evidence, robustness to non-semantic confounds, and compatibility with regression-style inference over semantic directions...
| Authors | Hubert Plisiecki |
|---|---|
| Primary category | cs.CL |
| Published | 10 March 2026 |
| Last updated at source | 10 March 2026 |
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