Medium — Data Science tag

Medium — Data Science tag enters the discovery pipeline as an RSS 2.0 feed — the publisher pushes new posts; we poll on cadence. Content lands in the ingestion pipeline, where it is classified against the taxonomy and written to a human-reviewed draft queue — nothing auto-publishes.

Fetched daily. Authority weight 3/5 feeds the source-quality score, so content from vetted publishers ranks higher before any human review. Discovered items are not yet routed to a default category.

No drafts recorded yet. New sources enter the rotation and produce drafts on their first successful fetches — absence here means not yet crawled into a draft, not absence of content.

Every registry claim should be checkable at its origin: https://medium.com/feed/data-science. If this source ever becomes unreachable, it is skipped with a warning — the pipeline records the failure and the rest of the run continues.

FETCH → EXTRACT → CLASSIFY → DRAFT. A weekly GitHub Action runs the discovery loop, opens one PR per batch, and a human curator accepts, edits or rejects each item. Scores are computed, never hand-written — see the scoring model and verification levels.