# CAPC-CG: A Large-Scale, Expert-Directed LLM-Annotated Corpus of Adaptive Policy Communication in China

arXiv: https://arxiv.org/abs/2510.08986 · HTML: https://mlsanity.com/paper/2510.08986/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 42 references that are in the map.
- First posted on arXiv 10 Oct 2025.

## Builds on (most cited first, 14 of 14)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Large Language Models for Data Annotation and Synthesis: A Survey](https://mlsanity.com/paper/2402.13446/) (2024)
- [CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction](https://mlsanity.com/paper/1807.02478/) (2018)
- [Lawformer: A Pre-trained Language Model for Chinese Legal Long Documents](https://mlsanity.com/paper/2105.03887/) (2021)
- [WanJuan: A Comprehensive Multimodal Dataset for Advancing English and Chinese Large Models](https://mlsanity.com/paper/2308.10755/) (2023)
- [Large Language Model Hacking: Quantifying the Hidden Risks of Using LLMs for Text Annotation](https://mlsanity.com/paper/2509.08825/) (2025)
- [CLUECorpus2020: A Large-scale Chinese Corpus for Pre-training Language Model](https://mlsanity.com/paper/2003.01355/) (2020)
- [LEVEN: A Large-Scale Chinese Legal Event Detection Dataset](https://mlsanity.com/paper/2203.08556/) (2022)
- [CJRC: A Reliable Human-Annotated Benchmark DataSet for Chinese Judicial Reading Comprehension](https://mlsanity.com/paper/1912.09156/) (2019)
- [OpenCSG Chinese Corpus: A Series of High-quality Chinese Datasets for LLM Training](https://mlsanity.com/paper/2501.08197/) (2025)
- [ChineseWebText: Large-scale High-quality Chinese Web Text Extracted with Effective Evaluation Model](https://mlsanity.com/paper/2311.01149/) (2023)
- [Best Practices for Managing Data Annotation Projects](https://mlsanity.com/paper/2009.11654/) (2020)
- [Empowering Users in Digital Privacy Management through Interactive LLM-Based Agents](https://mlsanity.com/paper/2410.11906/) (2024)

## Built on by (most cited first, 1 of 1)

- [Experts Rise Where LLMs Disagree: Using Cross-Model Disagreement to Target Expert Effort in LLM Codebook Revision for Large-Scale Annotation](https://mlsanity.com/paper/2609.26926/) (2026)

## Abstract (arXiv)

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 taxonomy of clear and ambiguous language categories, building on Ang's theory of adaptive policy communication. Spanning 1949-2023, this corpus includes national laws, administrative regulations, and ministerial rules issued by China's top authorities. Each document is segmented into paragraphs, producing a total of 3.3 million units. Alongside the corpus, we release comprehensive metadata, a two-round labeling framework, and a gold-standard annotation set developed by expert and trained coders. Inter-annotator agreement achieves a Fleiss's kappa of K = 0.86 on directive labels, indicating high reliability for supervised modeling. We provide baseline classification results with several large language models (LLMs), together with our annotation codebook, and describe patterns from the dataset. This release aims to support downstream tasks and multilingual NLP research in policy communication.