@inproceedings{sun-etal-2026-capc,
    title = "{CAPC}-{CG}: A Large-Scale, Expert-Directed {LLM}-Annotated Corpus of Adaptive Policy Communication in {C}hina",
    author = "Sun, Bolun  and
      Chang, Charles  and
      Ang, Yuen Yuen  and
      Mu, Ruotong  and
      Xu, Yuchen  and
      Zhang, Zhengxin  and
      Hao, Pingxu",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.42/",
    pages = "944--966",
    ISBN = "979-8-89176-390-6",
    abstract = "We introduce CAPC-CG, the Chinese Adaptive Policy Communication (Central Government) Corpus, the first open dataset of Chinese policy directives annotated with a five-color typology of policy signals, capturing clarity and ambiguity, grounded in the theory of adaptive policy communication. Spanning 1949{--}2023, this corpus includes laws, regulations, and rules issued by Chinese central authorities, segmented into 3.3 million paragraph units. We further propose and validate an expert-directed LLM annotation method that integrates codebook design, structured training, a two-step workflow, and LLM-based scaling. Alongside the corpus, we release metadata and a gold-standard labeled set developed by trained coders. Inter-annotator agreement achieves a Fleiss' kappa of {\ensuremath{\kappa}} = 0.86 on directive labels, indicating high reliability. We provide baseline classification results with several large language models (LLMs), together with our codebook, and describe patterns from the data. This release enables downstream tasks and multilingual NLP research in communication strategies under complexity and uncertainty."
}