Four pillars. Cohort-percentile thresholds. Verbatim verifier. Citation tokens with freshness timestamps. The grade refuses to compute when the source can't be verified — and says so.
PublicWeave is built on five non-negotiable rules. Every UI surface — report cards, peer comparisons, the WeaverAI assistant — must respect them.
If the dollar figure isn't physically present in the underlying audited filing, the platform refuses to assert it. No inference, no LLM rounding, no "approximately."
Every row carries an audit tier. Tier A = fully verified verbatim from a primary source. Tier B = partial gaps documented. Tier C = held in staging for review. The chip is on the report card.
Every WeaverAI answer carries [ref:source@2026-04-30T12:00Z] citations. The verifier refuses unknown citations. Source freshness is checked against the live data_source_state registry on every request.
Below 80% of expected inputs present, the grade publishes with a "provisional" chip. No silent gaps. Visible to every reader, every export, every shared report.
When peer cohort is below the min-30 floor, when a source is stale beyond its SLA, when an audit hasn't been filed — the platform shows the gap. It doesn't fill it with a guess.
Run the same inputs through services/grade_engine.py and you get the same letter. Pure cohort math, golden tests, no LLM in the grading loop. Reviewable by an auditor without us in the room.
Cohort-percentile thresholds — A is the top decile of your peer cohort, F is the bottom. No absolute floors that age. No subjective weighting.
Operating revenue ÷ operating expenditures. Excludes bond proceeds, transfers, capital grants. The rating-agency-grade definition of operating self-sufficiency.
Net surplus or deficit relative to operating spending, derived from revenue − expenditures (not the extracted "Change in Net Position"). Caught the Schaumburg AHPD board-memo bug — see live grade.
All-funds capital investment as a share of total spending. Includes bond-funded capital from capital_plan_projects when published — captures investment outside the operating budget.
Debt service ÷ operating spending. Cohort-percentile against same-type same-state peers. Bond paydown years (one-time drops) are flagged via agency notes — see SPD FY2026 paydown context for the pattern.
{entity_type} · {state} · {size_quintile}. A 200-acre Illinois park district is ranked against other 200-acre Illinois park districts — not against a 5,000-acre municipal system in California.
If the state cohort has fewer than 30 peers, the cohort widens (drop size quintile, then drop state, then drop entity type) until 30 are present. Citation per Wang/Dennis/Tu 2007.
Top and bottom 5% of the cohort are clipped before percentile computation, so a single billion-dollar Houston outlier doesn't crush the rest of the cohort. Citation per Mead 2001.
The grade card shows your percentile and a 95% confidence band ("87 — 96"). When the band is wide, the cohort is small. Citation per Efron 1979.
No LLM-fabricated anomalies. Every "watch" or "alert" on a report card is a SQL pattern with named source columns and a clear severity rule.
Total revenue dropped while tax levy rose — likely fund-scope mismatch or one-time loss.
Most-recent audited filing is N years old. Newer CAFR upload tightens grade confidence.
Revenue per capita is N standard deviations from cohort mean.
Capital expenditures jumped sharply year-over-year without a published capital plan.
Federal Audit Clearinghouse flagged a going-concern paragraph or material weakness.
Unassigned fund balance went negative — GFOA recommends 2+ months operating reserves.
Debt service exceeds peer-cohort 90th percentile.
Extraction failure or missing input flagged for human triage.
No proprietary scoring. No rating-agency-equivalence claims. Every threshold cites a published standard.
Bond-rating criteria inform Debt Service Coverage (1.2× minimum) and structural-balance thresholds.
Government Finance Officers Association best-practice guidance on fund balance, capital ratios, debt limits, GASB 54 classification.
Park-specific benchmarks: cost recovery (30–40%), capital reinvestment, program revenue. Peer medians published nationally.
Illinois State Board of Education Financial Profile + NCES F-33 federal school finance survey for school district models.
Park districts, libraries, and municipalities use the four-pillar model. School districts use a separate scorecard built from federal and state academic data.
Four-pillar model: Recovery (30) + Balance (25) + Capital (20) + Debt (25) = 100. Cohort-percentile thresholds against same-type same-state peers. Park districts surface a Cost Recovery Rate sub-metric (NRPA 30–40%); libraries weight Per-Capita Investment more heavily.
A separate model: Academic (30) + Climate (20) + Teaching (20) + Fiscal (15) + Equity (15) = 100. Built from the Illinois Report Card public data set + NCES F-33 federal survey covering all 14,536 US school districts. See sub-metrics on the School Districts page.
Each source is registered in data_source_state with last-fetch and last-success timestamps. WeaverAI cites the timestamp on every answer; the verifier refuses unknown citations.
Illinois Comptroller · CA SCO ByTheNumbers · Indiana Gateway · Ohio Auditor · Colorado OSA — primary audited source for governmental-funds totals.
Annual Survey of School System Finances — 14,536 LEAs nationwide, FY2022–2023, structured federal data covering revenue and expenditure by source/function.
LEA → Place geographic overlay (TIGER 2023) — 55,224 LEA-place edges. Federally authoritative source for cross-agency relationships.
Public Libraries Survey — 9,252 libraries nationwide. Visits, circulation, staffing, collection counts.
Single audit findings — going-concern paragraphs, material weaknesses, significant deficiencies. Refreshed hourly via stateful resumer.
Illinois State Board of Education public data set — 858 districts, academic + financial profile, zero API cost.
24,887 government units indexed. Primary registry for entity identification and population.
ActiveNet · Brightly — for districts that integrate, programs / registrations / facilities / asset conditions feed directly into operational sub-pillars.
Agency-submitted: Audited CAFRs, budget books, community surveys — uploaded by directors to improve their grades and correct extraction errors. Routed through Tier-A/B/C gates before they hit the live grade.
Director-confirmed context: Agency notes (bond paydown explanations, capital cycle context, restructuring events) are human-vetted before they appear next to a grade. They never change the math — only the interpretation.
Never used as primary data: Survey opinions · trade-press rankings · crowdsourced reviews · LLM speculation. The platform refuses to fabricate.