Methodology
How we calculate scores, detect conflicts, and ensure fairness. Every formula and threshold is documented here.
Open Methods. No Black Boxes.
Core Principles
Objectivity
All scores derive from verifiable data and transparent formulas. No editorial judgment influences individual scores.
Transparency
Every methodology detail is public. Users can see exactly how each score is calculated and what data sources are used.
Fairness
Politicians are compared within peer groups (same chamber, similar tenure). Balanced assessment considers both sides of every conflict.
Data-Driven
Scores update as new data arrives. No manual overrides. AI-generated content is clearly labeled with confidence levels.
Grading System
Scores are mapped to letter grades on a 0–100 scale. Colors indicate performance bands.
| Grade | Score Range | Description | Color |
|---|---|---|---|
| A+ | 97–100 | Exceptional | |
| A | 93–96 | Excellent | |
| A- | 90–92 | Very Good | |
| B+ | 87–89 | Good | |
| B | 83–86 | Above Average | |
| B- | 80–82 | Slightly Above Average | |
| C+ | 77–79 | Average | |
| C | 73–76 | Below Average | |
| C- | 70–72 | Slightly Below Average | |
| D+ | 67–69 | Poor | |
| D | 63–66 | Very Poor | |
| D- | 60–62 | Extremely Poor | |
| F | 0–59 | Failing |
10 Scoring Dimensions
Each politician is evaluated across 10 dimensions. Dimensions with insufficient data are excluded from the composite score.
D1Promise Fulfillment
Weight: 15%Rate at which a politician follows through on campaign promises and public commitments
Sources: Campaign speeches, press releases, official statements, legislative records
D2Specificity & Clarity
Weight: 8%How specific, measurable, and actionable the politician's commitments are
Sources: Same as D1 — evaluated at extraction time
D3Financial Independence
Weight: 10%Degree of concentration or diversification in funding sources
Sources: FEC filings, OpenSecrets, state disclosure databases
D4Conflict Exposure
Weight: 12%Inverse of total conflict flag severity — fewer and less severe flags yield higher scores
Sources: Cross-referencing votes, trades, donors, and committee assignments
D5Voting Consistency
Weight: 12%Alignment between stated positions/promises and actual votes
Sources: Congress.gov roll call data, promise-vote linkage analysis
D6Legislative Productivity
Weight: 10%Bills introduced, cosponsored, and passed relative to chamber peers
Sources: Congress.gov legislative data, GovTrack statistics
D7Constituent Alignment
Weight: 12%Degree to which voting behavior matches district/state preferences
Sources: District polling data, Cook PVI, constituent surveys
D8Attendance & Participation
Weight: 6%Vote participation rate and committee hearing attendance
Sources: Official chamber attendance records, committee logs
D9Transparency & Disclosure
Weight: 7%Timeliness and completeness of financial disclosures
Sources: STOCK Act filings, financial disclosure reports, OGE records
D10Bipartisan Effectiveness
Weight: 8%Cross-party collaboration on bills and amendments
Sources: Congressional cosponsorship data, bipartisan index scores
Composite Score
The composite score is a weighted average of all available dimensions. At least 5 dimensions must have data for a composite score to be generated.
Weight Presets
Users can switch between focus presets to weight dimensions differently.
| Preset | D1 | D2 | D3 | D4 | D5 | D6 | D7 | D8 | D9 | D10 |
|---|---|---|---|---|---|---|---|---|---|---|
| Balanced | 15% | 8% | 10% | 12% | 12% | 10% | 12% | 6% | 7% | 8% |
| Anti-Corruption Focus | 8% | 5% | 20% | 25% | 10% | 5% | 5% | 5% | 15% | 2% |
| Effectiveness Focus | 20% | 5% | 5% | 5% | 10% | 20% | 10% | 10% | 5% | 10% |
| Representation Focus | 20% | 5% | 5% | 5% | 20% | 5% | 25% | 10% | 3% | 2% |
| Transparency Focus | 5% | 15% | 20% | 20% | 5% | 3% | 5% | 5% | 20% | 2% |
Interactive Formula Explorer
Conflict Detection
Eight conflict patterns are monitored across five primary (P1–P5) and three secondary (S1–S3) categories. Each pattern has specific trigger conditions and mitigating factors.
P1Donor-Vote Alignment
significantTrigger: Votes benefiting major donor sectors within 90 days of large contribution
Mitigating factors: Small donor ratio, party-line vote, broad bipartisan support
P2Stock-Committee Overlap
noteworthyTrigger: Securities holdings in sectors under committee jurisdiction
Mitigating factors: Blind trust, index fund, pre-scheduled trade, broad market ETF
P3Stock-Vote Timing
criticalTrigger: Trades within 30 days before/after votes affecting the security's sector
Mitigating factors: Pre-scheduled, blind trust, loss-generating trade, broad index
P4Business-Legislation Connection
significantTrigger: Introducing or championing bills benefiting businesses with financial ties
Mitigating factors: Broad economic benefit, constituent demand, bipartisan effort
P5Donor-Committee Jurisdiction
noteworthyTrigger: Significant donations from sectors under the politician's committee jurisdiction
Mitigating factors: Industry-standard levels, constituent employers, small donor offset
S1Donor-Legislation Beneficiary
significantTrigger: Legislation directly benefiting specific donor entities
Mitigating factors: Broad beneficiary pool, consumer benefit, prior advocacy record
S2Revolving Door
noteworthyTrigger: Post-service employment patterns with previously regulated entities
Mitigating factors: Cooling off period compliance, different sector, public service track record
S3Bundled Influence
criticalTrigger: Multiple conflict patterns co-occurring for the same entity/sector
Mitigating factors: Independent verification of each sub-pattern, context assessment confirms routine
Severity Thresholds
Flags de-escalate one severity level when 2+ mitigating factors are confirmed present.
Data Source Tiers
Data is classified into four reliability tiers. Higher-tier data takes precedence in conflicts.
Government Records
Congress.gov, FEC filings, OGE disclosures, STOCK Act reports, official press releases
Primary authoritative data — highest confidence
Regulatory & Watchdog
OpenSecrets, GovTrack, ProPublica Congress API, CRP sector classifications
Curated secondary data — high confidence with cross-reference
AI-Extracted
Campaign speech transcripts, press conference analysis, social media statements
Claude-extracted with confidence scoring — requires verification
Community
User submissions via moderated community contribution pipeline
Multi-stage review (automated → AI → peer → staff) before incorporation
Fairness Safeguards
Balanced Assessment
Every conflict flag includes both a "case against" and "case for" the politician, generated by AI with structured prompts that mandate both perspectives.
Prohibited Language
Scoring and assessment text is screened for partisan language, inflammatory terms, and editorializing. Only factual, neutral language is used in scores and summaries.
Peer Comparison
Individual metrics are contextualized against chamber percentiles. A politician isn't penalized for behaviors common across all members (e.g., industry-standard PAC contributions).
Mitigating Factors
Each conflict pattern has specific mitigating factors that are actively checked. When 2+ are present, severity is automatically de-escalated by one level.
Known Limitations
Data Gaps
Not all politicians have complete data across all 10 dimensions. State-level officials may have fewer federal data sources. The composite score adjusts by only averaging available dimensions.
AI Confidence
AI-extracted promises and context assessments carry confidence scores. Lower-confidence extractions are flagged and weighted accordingly. Community verification helps improve accuracy over time.
Timing Lag
Financial disclosure data can lag by 30–45 days. Vote data is typically available within 24 hours. Promise status updates depend on legislative calendar and news cycle coverage.
Causation vs. Correlation
Conflict flags identify patterns of potential concern, not proven wrongdoing. A donor-vote alignment flag means the pattern exists, not that the donation caused the vote.