This paper describes our submission to SemEval-2026 Task 10: PsyCoMark, which addresses span-level identification of psycholinguistic conspiracy markers and document-level conspiracy classification. For Subtask 1, we fine-tune several pretrained transformer encoders and analyse their behaviour under different training configurations. For Subtask 2, we develop a hybrid system that combines ModernBERT-large with surface-level linguistic features.Our results show that straightforward fine-tuning of strong pretrained models is more effective than more complex pipelines and that additional handcrafted features do not yield consistent improvements. On the official test set, we rank 18th in Subtask 1 (overlap-based macro F1 = 0.16) and 20th in Subtask 2 (macro F1 = 0.76).