This paper proposes a selective multilingual and multitask fine-tuning strategy for online polarization detection that improves cross-lingual stability over fully joint training. Covering all three subtasks — polarization detection (POLARDETECT), polarization type classification (POLARTYPE), and rhetorical manifestation identification (POLARMANIFEST) — across all 22 languages of the shared task, the approach introduces controlled specialization, where languages and subtasks are grouped empirically and separate specialist models are fine-tuned for each subset. Restricting parameter sharing substantially improves performance even without ensemble averaging, whereas ensembling jointly trained models fails to mitigate instability. The final specialist ensemble improves Task 3 macro-F1 from 0.3330 to 0.4920 and reduces cross-lingual dispersion (CV: 0.613 → 0.321). Under the official ranking framework, the system ranks 7th among 16 submissions with complete multilingual and multitask coverage and remains within 5% of the best system in 37.70% of evaluation conditions.