Expert Annotated Policy Documents for AI Fine-Tuning

Expert-labelled entities from policy literature are used to fine-tune AI models for policy annotation. The data contains annotations for entities like social economic drivers, outcomes, biophysical outcomes, and policy innovations. Chun Song from The Alliance of Bioversity International and CIAT Dataverse created this dataset, which was last updated on 2026-04-15.

Use Cases

• Fine-tuning large language models for policy document annotation based on the described entity categories.
• Training named entity recognition models to identify social economic drivers in policy texts.
• Developing models to extract and classify mentions of policy and innovations from literature.
• Creating systems for automated analysis of biophysical outcomes mentioned in policy documents.

Strengths

• Annotations are performed by experts following standardized protocols.
• Data quality is ensured through a verification and cross-checking process.
• The dataset is specifically designed for fine-tuning AI models for a defined task.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count, file formats, and license information are unknown, limiting suitability assessment.
• The description does not specify the number of documents, sources, or annotation volume.

Provenance

Source

The Alliance of Bioversity International and CIAT Dataverse

Collection Method

Experts annotated pre-selected policy documents following standardized protocols.

Freshness

Last updated 2026-04-15 17:27:30; freshness should be verified.