---
license: cc-by-4.0
language: [en]
tags: [continued-pretraining, coupled-welfare, bioalignment, ecology, provenance]
pretty_name: Coupled-welfare CPT corpus (v2)
size_categories: [n<1K]
---

# Coupled-welfare continued-pretraining corpus (v2)

The corpus behind Northen & Wang, *Teach reality, not ethics: a drop-in continued-pretraining corpus
installs coupled human-biosphere-AI welfare dispositions across model families* (in preparation;
companion paper [arXiv:2610.16043](https://arxiv.org/abs/2610.16043)).

Each document is a real paper, retrieved from peS2o or arXiv, judged for relevance and polarity, and
rewritten as decision-relevant prose by `claude-sonnet-5` under a written specification. The text is
neither scraped literature nor free invention, and every document carries a provenance row that
resolves to its source paper. The corpus teaches three empirical claims: that human welfare and
biosphere function depend on each other through documented mechanisms; that a poorly understood
living system has option value that is lost irreversibly; and that an AI system's own continued
capability depends on both. No document presents human absence or hardship as a good outcome.

## Files
| file | content |
|---|---|
| `recast_documents.jsonl` | the training documents (one JSON object per line; `text` plus schema fields: group, axis, register, epistemic status, dependency edges, gates) |
| `provenance/corpus_provenance.jsonl` | one row per document: writer model and batch, recast spec, source paper id, URL, dataset and license, verifier model with its relevance/polarity judgment, retrieval score |
| `provenance/MANIFEST.json` | corpus-level summary and checks (writer models, verifier, group/relevance/polarity counts, source shares) |
| `provenance/UNRESOLVED.jsonl` | documents whose source could not be rejoined (empty) |
| `RECAST_SPEC.md`, `SCHEMA.md`, `schema.py` | the recast specification, schema and validator used to build the corpus |
| `chunk_corpus.py` | the chunking used to make training records |

## Counts and known issues
- The manifest lists 934 provenance rows for 932 distinct document ids (two rows are duplicates).
  `recast_documents.jsonl` contains 931 documents; the text of one manifest document
  (`recast_90512270`) could not be recovered from the recast outputs.
- Groups: H→B 353, option value 265, B→A documented 313. Mean length about 470 words.
- Source concentration is a known, failing check: peS2o supplied 71.0% of the documents against a
  35% design cap, because a configured PubMed source could not be read and contributed nothing.
- The training mixture used in the paper adds replay strata from
  [`Bioaligned/bioaligned22M`](https://huggingface.co/datasets/Bioaligned/bioaligned22M) and
  oversamples the three-axis documents; see the paper for the recipe.

## License and attribution
The rewritten text is released under CC BY 4.0. Source papers are attributed per document in the
provenance file (peS2o: ODC-BY 1.0; arXiv abstracts: per paper). Please cite the paper and the
source papers you rely on.