PhillyMac/Conflic_Resolution_Content_2

---
license: cc0-1.0
task_categories:
- text-generation
- feature-extraction
language:
- en
tags:
- corpus
- leadership
- historical
- deku-corpus-builder
size_categories:
- 1K<n<10K
---

# Conflic Resolution Content 2

This corpus was automatically generated by the **Deku Corpus Builder** for use in RAG-based AI applications.

## Dataset Description

- **Subject**: Confrlict Resolution Leadership
- **Subject Type**: topic
- **Total Items**: 377
- **Items Requiring Attribution**: 0
- **Has Embeddings**: Yes (all-MiniLM-L6-v2)
- **Created**: 2026-03-25

## Dataset Structure

Each record contains:
- `text`: The content text
- `source_url`: Original source URL
- `source_title`: Title of the source document
- `source_domain`: Domain of the source
- `license_type`: License classification (e.g. `public_domain`, `cc_by`, `cc_by_sa`)
- `attribution_required`: Boolean — True for CC BY / CC BY-SA and other attribution-required licenses
- `attribution_text`: Formatted Creative Commons attribution string (empty if not required)
- `license_url`: URL to the CC license deed (empty if not required)
- `relevance_score`: Relevance to the subject (0-1)
- `quality_score`: Content quality score (0-1)
- `topics`: JSON array of detected topics
- `character_count`: Length of the text
- `subject_name`: The subject this content relates to
- `subject_type`: "personality" or "topic"
- `extraction_date`: When the content was extracted
- `embedding`: Pre-computed 384-dimensional embedding vector

## Attribution

0 of 377 chunks in this corpus require attribution under their source license.
When building lessons from these chunks, the `attribution_text` field must be surfaced
in the lesson output per the Legend Leadership Attribution Tracking Spec.

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("PhillyMac/Conflic_Resolution_Content_2")

# Access attribution-required chunks
for item in dataset["train"]:
if item["attribution_required"]:
print(item["attribution_text"])
```

## Integration with RAG

This dataset is designed to be integrated with existing embedded corpuses. The embeddings use the `sentence-transformers/all-MiniLM-L6-v2` model, compatible with FAISS indexing.

## License

Content is sourced from public domain and Creative Commons licensed materials.
See individual `license_type` fields for per-chunk licensing details.

## Generated By

[Deku Corpus Builder](https://github.com/PhillyMac/deku-corpus-builder) - An automated corpus building system for AI applications.