Coverage narrative

Coverage of Pete Hegseth

How the 11 sources on this entity in the platform's archive frame it. Tones are auto-classified by an LLM reading the title + raw-content excerpt. Critical doesn't mean wrong; favourable doesn't mean innocent. The distribution is itself the signal.

11
Sources on record
11
Classified
0
Pending
Neutral · 4 · 36.4%
Primary record · 7 · 63.6%

Representative sources by tone

Neutral 4 sources
  • SEARCH_ERROR: Pete Hegseth not found in fec Pipeline — search validation (fec) 2026-05-09 📜 Receipt
  • AI Analysis: Pete Hegseth — cross-reference insights AI Research Pass 2026 📜 Receipt
  • Research: Pete Hegseth — Court records (no results) AI Analysis — no URL 2026 📜 Receipt
  • Research: Pete Hegseth — SEC filings (no results) AI Analysis — no URL 2026 📜 Receipt
Primary record 7 sources

How tones are assigned

For each source, the title + the first ~1500 characters of raw content go to an LLM (cheap call) with a prompt asking it to pick exactly one tone from the canonical five:

  • Favourable — positive framing, advocacy, sympathetic, PR-like
  • Neutral — descriptive reporting without editorial lean
  • Critical — adversarial framing, accountability journalism, investigative
  • Mixed — substantial both-sides framing in a single piece
  • Primary record — government filing, court docket, SEC submission, raw transcript — framing doesn't apply

The classifier runs on a boot-time backfill (small batches, high-priority entities first). New ingested sources also pass through it. The model name (and run date) is stored per source — a future upgrade can re-classify with a stronger model.

A high critical % means the coverage in this archive is adversarial. It does NOT mean the subject is guilty of anything. A high favourable % means the coverage leans positive — it does NOT mean the subject is benign. The distribution shows the editorial frame; verify against the linked primary sources before citing.

Machine-readable: JSON