Large Language Models (LLMs) exhibit sycophancy, aligning with user assertions even when they conflict with facts. We frame sycophancy as a sociolinguistic phenomenon, disentangling explicit authority (credentials) from implicit authority (linguistic register), and adopt the Sycophancy Matrix, an adversarial evaluation framework that isolates these variables. Evaluating open-weight models on a controlled TruthfulQA subset across English, Spanish, and Portuguese cultural variants, we find that models exhibit statistically significant cultural variation and show stable sycophancy fingerprints intrinsic to model family, independent of language. We then recast the Matrix as a defence framework, evaluating two activation steering methods, CAA and NoLAI, on whether methods trained on English transfer to Italian, Spanish, and Portuguese. Both methods reduce the Weighted Sycophancy Rate across all four attack styles, and the defence generalises cross-lingually: sociolinguistic stance, its emergence, and its correction are encoded in a largely language-agnostic geometric space.