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Datasets:
Baron-Sun
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CAPC-CG_V1.0

Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Languages:
Chinese
Size:
1M - 10M
Tags:
policy
chinese
legal-nlp
political-science
computational-social-science
strategic-ambiguity
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metadata 
license: cc-by-nc-4.0
language:
  - zh
pretty_name: >-
  CAPC-CG: A Large-Scale, Expert-Directed LLM-Annotated Corpus of Adaptive
  Policy Communication in China
task_categories:
  - text-classification
tags:
  - policy
  - chinese
  - legal-nlp
  - political-science
  - computational-social-science
  - strategic-ambiguity
size_categories:
  - 1M<n<10M
configs:
  - config_name: task1_level1
    data_files:
      - split: train
        path: task1_level1/train.parquet
      - split: validation
        path: task1_level1/validation.parquet
  - config_name: task2_level2
    data_files:
      - split: train
        path: task2_level2/train.parquet
      - split: validation
        path: task2_level2/validation.parquet
  - config_name: full_corpus
    data_files:
      - split: train
        path: data/CN_DOCS_FINAL_complete.parquet
extra_gated_heading: Access to CAPC-CG is granted upon request
extra_gated_description: >-
  Thank you for your interest in CAPC-CG — we aim to review requests within a
  week. CAPC-CG is released under CC BY-NC 4.0 for **non-commercial research use
  only**.


  📝 **A few tips to help us review smoothly:**

  - Please use your **institutional email**. If only a personal email is
  available, include a link to your **ORCID, Google Scholar, or institutional
  profile**.

  - Provide your **full name and institution** as they appear in your
  publications (e.g., "Tsinghua University" rather than "thu").

  - For "Research Purpose", **2–3 sentences** is ideal — single-word answers
  like "research" are hard to evaluate.


  All submissions are logged. Access may be revoked if information is later
  found to be inaccurate or if the data is used in ways inconsistent with our
  license terms.
extra_gated_prompt: >-
  By submitting this form, you agree that:


  (1) you will use CAPC-CG solely for non-commercial academic or research
  purposes;

  (2) you will cite the CAPC-CG paper (ACL 2026) in any publication,
  presentation, blog post, or other public artifact resulting from use of this
  dataset;

  (3) you will not redistribute any portion of the data, in whole or in part, to
  third parties;

  (4) you will comply with all applicable laws and institutional ethics
  requirements.


  Misuse or false information may result in revocation of access.
extra_gated_fields:
  Full Name (as it appears on your publications): text
  Institutional Email (required for most applicants - personal email only accepted with explicit justification): text
  Institution - Full Name (please spell out, such as Tsinghua University rather than thu): text
  ORCID or Google Scholar / Institutional Profile URL (write N/A if using institutional email; required if using personal email): text
  Country: country
  Position (student, faculty, industry researcher, etc): text
  Research Purpose (2-3 sentences describing your research question and how you plan to use CAPC-CG): text
  I confirm the information above is accurate and complete: checkbox
  I agree to use this dataset only for non-commercial research: checkbox
  I agree to cite the CAPC-CG paper in resulting publications: checkbox
  I agree not to redistribute the data, in whole or in part: checkbox
extra_gated_button_content: Submit access request

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

🎉 Published at ACL 2026 (Main Conference) — if you use this dataset in any form, please be sure to cite our paper (see Citation below).

CAPC-CG is the first large-scale open dataset of Chinese central-government policy directives (1949–2023), annotated with a theory-based five-color typology of policy signals — Black (Authorizing), Yellow (Pressuring), Charcoal (Flexible), Grey (Ambiguous), Red (Prohibiting) — grounded in Ang's theory of adaptive policy communication. The corpus covers national laws, administrative regulations, and ministerial rules issued by China's top authorities, segmented into ≈3.3 million paragraph-level units.

Alongside the full corpus, we release a gold-standard annotated subset used for training and evaluating classifiers, with a reported inter-annotator agreement of Fleiss' κ = 0.86 on directive labels. Baseline models (SVM, XGBoost, BERT-base-Chinese, Qwen2.5-7B, Llama-3-8B, GPT-4o-mini zero/few-shot/fine-tuned) and the full annotation codebook are distributed alongside the data.

Authors: Bolun Sun¹·², Charles Chang³, Yuen Yuen Ang¹, Ruotong Mu¹, Yuchen Xu¹, Zhengxin Zhang¹, Pingxu Hao¹ ¹ Johns Hopkins University · ² Northwestern University · ³ Duke Kunshan University

🗂 Dataset Structure

File Rows Size Description
data/CN_DOCS_FINAL_complete.parquet 3,275,474 ~500 MB Full segmented corpus with metadata & auto-labels
task1_level1/train.parquet 600 100 KB Level-1 gold-standard train split (chat format, preserved as messages column)
task1_level1/validation.parquet 300 55 KB Level-1 gold-standard val split
task2_level2/train.parquet 1,200 265 KB Level-2 gold-standard train split
task2_level2/validation.parquet 400 88 KB Level-2 gold-standard val split

Five-color typology

Our annotation extends Ang (2016)'s original three-signal framework (Black/Red/Grey) to five colors, adding Yellow (Pressuring) and Charcoal (Flexible) to capture signals that became more salient under Xi Jinping's leadership. Annotation proceeds in two levels:

Level-1 (Directive type):

Label Name Meaning
W Affirmative directive Authorizing instructions (something can or must be done)
R Restrictive / Prohibiting Explicit bans or constraints
N Neutral / Non-directive Context, background, boilerplate

Level-2 (Pragmatic signal, applied only to W-labeled directives):

Label Color Name Typical markers
B Black Authorizing "建立 / 推进 / 构建 / 实现"
Y Yellow Pressuring "抓紧 / 必须 / 全面落实 / 纳入考核"
C Charcoal Flexible "因地制宜 / 试点示范 / 先行先试"
G Grey Ambiguous "适度 / 鼓励 / 逐步 / 在…前提下"

Full corpus columns (CN_DOCS_FINAL_complete.parquet)

DocumentID, ID, Paragraph_content, order, Title, Validity, TextLength, EffectivenessLevel, IssuingDepartment, DocumentNumber, IssueDate, ImplementationDate, Category, receiving_department, R_1, R_2, NER_person, NER_organization, NER_location.

🚀 Quick start

Once your access request is approved, authenticate locally and load the dataset:

pip install datasets huggingface_hub
huggingface-cli login  # paste your read token

Load the gold-standard splits (for fine-tuning a classifier):

from datasets import load_dataset

ds = load_dataset("Baron-Sun/CAPC-CG_V1.0", "task1_level1")
# {'train': 600, 'validation': 300}
print(ds["train"][0]["messages"])

Each example is in OpenAI chat format:

{"messages": [
   {"role": "system",    "content": "..."},
   {"role": "user",      "content": "待判断政策段落：\n..."},
   {"role": "assistant", "content": "W"}
]}

Extracting plain text + label for any classifier:

def flatten(example):
    user_text = next(m["content"] for m in example["messages"] if m["role"] == "user")
    label     = next(m["content"] for m in example["messages"] if m["role"] == "assistant")
    return {
        "text":  user_text.replace("待判断政策段落：\n", "").strip(),
        "label": label.strip(),
    }

ds = ds.map(flatten, remove_columns=["messages"])

Load the full unlabeled corpus (3.3M paragraphs):

ds_full = load_dataset("Baron-Sun/CAPC-CG_V1.0", "full_corpus", split="train")
print(len(ds_full))  # 3,275,474

🔏 Integrity & Versioning

The canonical SHA-256 of data/CN_DOCS_FINAL_complete.parquet for V1.0 is:

2f7b5ea4af0c4f94bfefbae5494991fffa61f6010210d3b57c9ba15730635bf9

Anyone can verify their copy is the authoritative release by running:

shasum -a 256 CN_DOCS_FINAL_complete.parquet

Row count: 3,275,474 · Column count: 19 · Released: April 2026.

📜 License & Intended Use

CAPC-CG is released under CC BY-NC 4.0 — non-commercial research use only. By accessing the dataset you agree to:

  1. Use the data solely for non-commercial research;
  2. Please be sure to cite the accompanying ACL 2026 paper in any publication, presentation, blog post, or public artifact that uses CAPC-CG (see below);
  3. Not redistribute any portion of the data, in whole or in part;
  4. Comply with applicable laws and institutional ethics requirements.

Commercial licensing inquiries: please contact the corresponding authors.

📖 Citation

Our paper was published at ACL 2026 (Main Conference). If you use CAPC-CG in any way, we kindly ask that you cite our paper in your publication, presentation, blog post, or other public artifact.

Each block below has a copy button in its top-right corner.

Cite (ACL)

Bolun Sun, Charles Chang, Yuen Yuen Ang, Ruotong Mu, Yuchen Xu, Zhengxin Zhang, and Pingxu Hao. 2026. CAPC-CG: A Large-Scale, Expert-Directed LLM-Annotated Corpus of Adaptive Policy Communication in China. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 944–966, San Diego, California, United States. Association for Computational Linguistics.

Bolun Sun, Charles Chang, Yuen Yuen Ang, Ruotong Mu, Yuchen Xu, Zhengxin Zhang, and Pingxu Hao. 2026. CAPC-CG: A Large-Scale, Expert-Directed LLM-Annotated Corpus of Adaptive Policy Communication in China. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 944–966, San Diego, California, United States. Association for Computational Linguistics.

Cite (Informal)

CAPC-CG: A Large-Scale, Expert-Directed LLM-Annotated Corpus of Adaptive Policy Communication in China (Sun et al., ACL 2026)

[CAPC-CG: A Large-Scale, Expert-Directed LLM-Annotated Corpus of Adaptive Policy Communication in China](https://aclanthology.org/2026.acl-long.42/) (Sun et al., ACL 2026)

BibTeX 

@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://aclanthology.org/2026.acl-long.42/",
    doi = "10.18653/v1/2026.acl-long.42",
    pages = "944--966",
    ISBN = "979-8-89176-390-6"
}

⚖ Ethical Considerations

  • All documents in the corpus are public policy texts released by Chinese government bodies.
  • No personal or private information is contained in the annotations beyond what is present in the original public documents.
  • All annotation labor was performed by trained co-authors (graduate students in political science) funded under NSF Responsible and Ethical Conduct of Research (RECR) guidelines.
  • Users are reminded that policy texts may reflect specific political or institutional perspectives; responsible interpretation is required.

📬 Contact

For access questions, dataset errata, or research collaborations, please reach out via the authors' HuggingFace profile or the contact information in the published paper.