Linguistic Inquiry and Word Count (LIWC) provides auditable psycholinguistic categories that are widely used to interpret depression-related language, but its incremental predictive role in modern multimodal systems remains unclear. We evaluate LIWC across two English and three Chinese depression-related corpora under matched participant-level cross-validation. We ask whether LIWC improves classification and what any performance change reflects. Intact LIWC is compared with three fold-local substitutes: a PCA-rotated version that removes direct access to named category coordinates, a participant-shuffled version that preserves real LIWC profiles while breaking participant alignment, and a random-marginal version that preserves feature-wise distributions. Across multiple fixed representation contexts, the results provide limited evidence for stable LIWC gains under frozen, participant-level early fusion. None of the prespecified dataset-blocked contrasts survives multiple-comparison correction. A separate SBERT calibration produces larger observed intact-versus-shuffled and intact-versus-random separations, indicating that larger participant-aligned signals can produce correspondingly larger separations under the same procedure, while not resolving the five-corpus power limitation. LIWC remains useful as an auditable, corpus-conditioned interpretive layer. These conclusions should not be generalized to fine-tuned, sequence-aware, or end-to-end architectures.