Datasets:

Bioaligned
/

coupled-welfare-corpus

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).

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 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.