Institutional repositories need scalable support for multilingual subject metadata assignment to long documents. On 19,710 SEDICI items and 37 labels, we compare 231 sparse/fixed-embedding configurations and two fine-tuned DistilUSE/LaBSE classifiers under separate threshold-selection, validation, and test partitions. Language-aware BM25 full text with LinearSVC achieved held-out Macro-F1 = 0.7522 (95% CI 0.7372–0.7643), Micro-F1 = 0.7685 (0.7567–0.7797), and Recall@3 = 0.9320; the LaBSE-initialized classifier reached Macro-F1 = 0.7069. A post-hoc BM25–LaBSE matched-exposure control, validation-only over three seeds with no test access, preserved the aggregate BM25 advantage. Strong sparse baselines therefore remain competitive under the evaluated Transformer protocols.