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Congress as a funding/compromise surface — committees of jurisdiction, non-linear bill flow, and the documented money-and-leverage map

Built 2026-06-12 from research/influence-congress-funding-compromise.json + data/congress_committees.json. Sources: House/Senate rules + Congress.gov (jurisdiction & bill histories), the Congressional Record / conference reports & the Federal Register (term-changes & resulting rulemakings), FEC + OpenSecrets (money), House Clerk / Senate financial disclosures incl. STOCK Act periodic transaction reports (personal holdings & trades), and Lobbying Disclosure Act filings (staff→lobbyist chains). fetch_fec.py wires the FEC API behind an env-gated key.

Evidentiary discipline (read first). Donation + vote alignment is correlation, not a bribe. Adjacency is not intent. This block builds a transparent leverage/exposure map from public records and grades every cell. It does not assert that any named member is "bought." "Degree of exposure" is a structural index (the rubric below), never a corruption verdict. All overlay edges (pac_funding, revolving_door, regulatory_legal) are excluded from the SCC/Z3/TLA+ proofs. Dollar figures not yet pulled from primary FEC/disclosure data are marked REQUIRES-INGEST and are not fabricated.

1. Why model Congress as a system, not a list

The financial/AI/surveillance fights are won or lost in legalese, in three moves the public record under-reports:

  1. Committees of jurisdiction are chokepoints. Each regulated industry's bills live in a specific committee; the membership of that committee is where the money concentrates by construction — finance PACs fund the Banking/Financial-Services seats regardless of who holds them.
  2. Bill flow is non-linear. A bill's operative terms are written, gutted, and re-inserted across subcommittee markup, manager's amendments, floor amendments, conference, and — the lowest-visibility vehicle — lame-duck omnibus attachments. The headline ("sweeping reform" / "modernization") is written at introduction; the terms that bind are often changed later, with far less coverage.
  3. Members carry documented financial exposure — industry donations, household securities/commodity/real-estate/debt positions, and post-office employment — that constitutes leverage, not proof of a quid pro quo.

2. The exposure rubric (a hypothesis generator, not a verdict)

A reproducible leverage/exposure index for a member on a policy domain. Every component is a public-record fact; the composite is explicitly an interpretation.

ComponentWhat it measuresStrength
C1Industry moneyshare of receipts from the regulated industry's PACs/employees vs. chamber median (FEC)weakest — industry funds the seat, not the disposition
C2Personal positionhousehold securities/commodity/real-estate/debt in the industry; trade timing vs. nonpublic committee info (STOCK Act PTRs)strong when timing is anomalous
C3Revolving doorprior/【post】-office employment by the industrystrongest, cleanest
C4Vote/term alignmentauthored amendments that change operative terms in the industry's favor (the misreported layer)strong
C5Family/affiliate & staffhousehold trades; former staff turned industry lobbyists; leadership-PAC overlapstructural
C6External leveragedocumented threats/kompromat/foreign-agent ties/debt to interested partieshigh-grade-required; mostly unsupported, left so

High composite = high structural exposure = incentives aligned by construction. It tells you where to read the primary record, not that a crime occurred. C3 and C2 survive scrutiny most often; C1 alone is near-noise.

3. Committees of jurisdiction → the industries they gate

(full membership rosters in data/congress_committees.json; chairs profiled in the Persons tab)

4. Bill flow — the non-linear, misreported term-changes (case studies)

These are the cases where the operative term entered or changed after the headline was written.

5. The documented money-and-leverage findings (graded)

6. Coverage & honest limits

Here now: the jurisdiction map, the non-linear bill histories with their misreported term-changes, the cleanest leverage findings, a transparent rubric, the committee-membership roster layer (data/congress_committees.json), and overlay edges into the bubble map (revolving-door / PAC, excluded from the proofs).

REQUIRES-INGEST (wired, not fabricated): per-member FEC/OpenSecrets dollar totals by industry across 26 years; full STOCK-Act PTR parsing; exhaustive staff→lobbyist (LDA) chains; per-member real-estate/debt disclosures. fetch_fec.py calls the FEC API behind an env-gated FEC_API_KEY (the FINRA pattern — no key in the repo) and tolerates absence, so the quantitative layer is reproducible.

By design: the full ~2,000-member roster across 13 Congresses is delivered as a roster-data layer + this analytical block, not as 2,000 hand-authored dossiers (the accepted roster-segmentation approach). The committee chairs and the highest-leverage members are profiled at depth in the Persons tab. Nothing here is a corruption verdict — it is a leverage/exposure surface for reading the primary record.

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