The detection of climate misinformation narratives remains challenging due to label imbalance, hierarchical taxonomies, and the multi-label nature of real-world claims. Developing models that can reliably assign fine-grained narrative categories is therefore essential for scalable analysis of climate disinformation. We present our approach to multi-label climate misinformation narrative classification for ClimateCheck@NSLP 2026 Task 2. The task requires assigning one or more narrative categories, defined by the hierarchical CARDS taxonomy, to climate-related claims. We investigate both encoder-based transformers and decoder-only large language models (LLMs), comparing fine-tuning BERT-based models with prompt-based and retrieval-augmented instruction tuning strategies with Qwen3 model. To address data scarcity and label imbalance, we explore targeted augmentation using external CARDS-based resources as well as semantic similarity filtering. Our experiments show that augmentation improves encoder-based models, with ModernBERT achieving competitive performance at low computational cost. However, the strongest results are obtained using retrieval-augmented instruction tuning with Qwen3, which narrows the candidate narrative space prior to prediction. This approach achieves a Macro-F1 score of 59.72% on the official test set, securing second place on the leaderboard. These findings demonstrate the effectiveness of retrieval-guided LLM adaptation for structured multi-label narrative classification while highlighting the continued relevance of efficient encoder-based models.

In disinformation datasets, narratives are often understood as recurring interpretive patterns that group texts under narrative labels. Recent work formalized narrative mining as inductively inferring narrative labels from corpora, but its evaluation stays tied to predefined taxonomies, a closed-world setting that cannot capture narratives absent from the reference labels. We introduce a three-tier evaluation framework for unsupervised narrative label generation: recovery (against a corpus’s own taxonomy), mining (against external label sets), and discovery (without predefined labels). Applying it, we compare clustering-based and graph-community-based pipelines across seven disinformation datasets, with human validation of discovery on two. The two families are complementary under automated metrics, but in a corpus with two prominent topics, clustering can reduce one topic to 2% of generated labels while graph-based pipelines stay balanced. Discovery validation also reveals many singletons (narrative labels derived from single claims, 30–62% of graph outputs), which clustering cannot produce. Annotators confirm many as recognizable disinformation narratives, suggesting that in open-world discovery the repetition assumed by narrative mining may be recognized outside the corpus, not within it. We release human-validated narrative candidate labels for the Climate Obstruction and PolyNarrative datasets to support taxonomy development and dataset extension.