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ai4data/fcv-data-use-paper

Dataset Card for FCV Data-Use Paper Companion dataset for the paper "Automated Tracking of Data Use in Fragile, Conflict, and Violence Settings: A Joint World Bank and UNHCR Multitask Extraction Framework" (Macalaba, Solatorio, Brock). Three-model swarm over the forced-displacement / FCV corpus: dataset-mention extraction (Call 1), relation extraction (Call 1b, 5 types), and impact/usage classification (Call 2, 3 tasks) -- plus the seed-based synthetic training data, the PRWP… See the full description on the dataset page: https://huggingface.co/datasets/ai4data/fcv-data-use-paper.

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Dataset Card

Dataset Card for FCV Data-Use Paper

Companion dataset for the paper "Automated Tracking of Data Use in Fragile, Conflict, and Violence Settings: A Joint World Bank and UNHCR Multitask Extraction Framework" (Macalaba, Solatorio, Brock).

Three-model swarm over the forced-displacement / FCV corpus: dataset-mention extraction (Call 1), relation extraction (Call 1b, 5 types), and impact/usage classification (Call 2, 3 tasks) -- plus the seed-based synthetic training data, the PRWP training subset, and the per-source original extractions.

All splits are published verbatim exactly as used in the paper.

Configs

ConfigSplits (records)
extractiontrain: 22794, validation: 415, test: 1706
relationtrain: 640, validation: 91, holdout: 21
impacttrain: 604, validation: 151, holdout: 84
synthetic-relationtrain: 976
sourcestrain: 5445

Schemas

  • extraction / sources: {"input", "output": {"entities": {"named_data", "descriptive_data", "vague_data"}, "entity_descriptions"}}.
  • extraction rows carry a meta field tagging provenance: FCV (forced-displacement / FCV corpus), PRWP (Policy Research Working Papers -- manually annotated + reviewed/validated), or synthetic (seed-based synthetic training data, flat-NER). train = FCV + PRWP + synthetic; validation and test are FCV-only.
  • synthetic-relation: relation-format synthetic training data ({"input", "output": {"entities": {"named_data", "organization"}, "relations": [{"<type>": {"head", "tail"}}]}, "_meta"}) from relation_training_synthetic and relation_llm_generated, with a source column.
  • relation: {"input", "output": {"entities": {"named_data", "organization"}, "relations": [{"<type>": {"head", "tail"}}]}, "_meta"} with types has_organization, used_by, has_acronym, has_timeframe, has_geography.
  • impact: {"input", "output": {"classifications": [{"task", "labels", "true_label"}]}} with tasks purpose_action, usage, typology.
  • sources: adds a source column (prwp, reliefweb, seis, esmap, refugee_pads) plus document_id and active_learning; the 57 active-learning docs are emitted as flat-NER rows (invalid mentions filtered by verdict) flagged active_learning: true.

Citation

bibtex
@misc{macalaba2026fcv,
  title={Automated Tracking of Data Use in Fragile, Conflict, and Violence Settings},
  author={Macalaba, Rafael and Solatorio, Aivin V. and Brock, Patrick Michael},
  year={2026},
  publisher={Hugging Face},
  journal={ai4data/fcv-data-use-paper}
}