Neutral · 3 · 50.0%
Primary record · 3 · 50.0%
Representative sources by tone
Neutral
3 sources
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Research: State of Florida v. Epstein, No. 50-2008-CF-009381-AXXX-MB — Parliamentary records (no results)
Search Scope Note
2026
📜 Receipt
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Research: State of Florida v. Epstein, No. 50-2008-CF-009381-AXXX-MB — Lobbying disclosures (no results)
Search Scope Note
2026
📜 Receipt
-
Research: State of Florida v. Epstein, No. 50-2008-CF-009381-AXXX-MB — SEC filings (no results)
Search Scope Note
2026
📜 Receipt
Primary record
3 sources
-
Research: State of Florida v. Epstein, No. 50-2008-CF-009381-AXXX-MB — Court records (no results)
Search Scope Note
2026
📜 Receipt
-
Research: State of Florida v. Epstein, No. 50-2008-CF-009381-AXXX-MB — Corporate registrations (no results)
Search Scope Note
2026
📜 Receipt
-
Research: State of Florida v. Epstein, No. 50-2008-CF-009381-AXXX-MB — 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