Coverage narrative

Coverage of Allegheny County Employees' Retirement System v. Palantir Technologies, Inc., et al.

How the 10 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.

10
Sources on record
6
Classified
4
Pending
Neutral · 5 · 83.3%
Primary record · 1 · 16.7%

Representative sources by tone

Neutral 5 sources
  • Research: Allegheny County Employees' Retirement System v. Palantir Technologies, Inc., et al. — Parliamentary records (no results) Search Scope Note 2026 📜 Receipt
  • Research: Allegheny County Employees' Retirement System v. Palantir Technologies, Inc., et al. — Court records (no results) Search Scope Note 2026 📜 Receipt
  • Research: Allegheny County Employees' Retirement System v. Palantir Technologies, Inc., et al. — Lobbying disclosures (no results) Search Scope Note 2026 📜 Receipt
  • Research: Allegheny County Employees' Retirement System v. Palantir Technologies, Inc., et al. — Corporate registrations (no results) Search Scope Note 2026 📜 Receipt
  • Research: Allegheny County Employees' Retirement System v. Palantir Technologies, Inc., et al. — USASpending contracts (no results) Search Scope Note 2026 📜 Receipt

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