README.md

---
license: cc-by-4.0
language:
- en
task_categories:
- token-classification
- text-classification
tags:
- data-use
- dataset-mention
- named-entity-recognition
- relation-extraction
- text-classification
- forced-displacement
- fcv
- synthetic-data
- active-learning
- world-bank
- unhcr
size_categories:
- 1K-10K
configs:
- config_name: extraction
  data_files:
  - split: train
    path: "extraction/train.jsonl"
  - split: validation
    path: "extraction/validation.jsonl"
  - split: test
    path: "extraction/test.jsonl"
- config_name: relation
  data_files:
  - split: train
    path: "relation/train.jsonl"
  - split: validation
    path: "relation/validation.jsonl"
  - split: holdout
    path: "relation/holdout.jsonl"
- config_name: impact
  data_files:
  - split: train
    path: "impact/train.jsonl"
  - split: validation
    path: "impact/validation.jsonl"
  - split: holdout
    path: "impact/holdout.jsonl"
- config_name: synthetic-relation
  data_files:
  - split: train
    path: "synthetic-relation/train.jsonl"
- config_name: sources
  data_files:
  - split: train
    path: "sources/train.jsonl"
---

# 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

| Config | Splits (records) |
|---|---|
| `extraction` | train: 22794, validation: 415, test: 1706 |
| `relation` | train: 640, validation: 91, holdout: 21 |
| `impact` | train: 604, validation: 151, holdout: 84 |
| `synthetic-relation` | train: 976 |
| `sources` | train: 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}
}
```