2019–2025 sentence-level text mining of 11 flagship AI governance documents. The dataset includes a reproducible frame-extraction pipeline and semantic network analysis, providing empirical evidence for a shift from capability to consequence framing. It was authored by Ying Wang and last updated on June 3, 2026.
Use Cases
- Conduct semantic network analysis based on the text mining of policy documents.
- Track framing shifts in AI governance discourse based on the described frame-extraction pipeline.
- Reproduce text mining analyses based on the provided reproducible pipeline.
- Study the evolution of AI policy language based on documents from 2019 to 2025.
Strengths
- Covers a defined time range from 2019 to 2025.
- Includes 11 flagship AI governance documents.
- Provides a reproducible frame-extraction pipeline.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
- The 1.4 MB size suggests a relatively small dataset.
Provenance
- Source
- figshare
- Collection Method
- Sentence-level text mining of published AI governance documents.
- Time Range
- 2019–2025
- Freshness
- Last updated 2026-06-03 14:01:25; freshness should be verified.