# E-E-A-T Evaluation Framework Use this rubric to evaluate content quality for competitive queries. ## Dimensions Score each dimension from 0 to 5: | Dimension | What to Check | |-----------|---------------| | Experience | First-hand evidence, practical examples, real usage context | | Expertise | Subject competence, technical accuracy, depth, specialist language used correctly | | Authoritativeness | Reputation signals, citations, recognized brand/person entities | | Trustworthiness | Accuracy, transparency, policy clarity, editorial rigor, safety disclosures | ## Query Stakes Classification Classify intent before scoring: - YMYL-high: finance, health, legal, safety - Commercial-high: high-ticket buying decisions - Informational-competitive: dense SERP competition with strong entities - Informational-low: low-risk, basic educational intent Raise trust requirements for higher-stakes intents. ## Evidence Checklist Review per template: 1. Authorship clarity: - named author - credentials or role - updated timestamp 2. Source quality: - primary sources cited - external corroboration - quote/context integrity 3. Editorial quality: - original analysis (not paraphrased commodity text) - internal consistency across pages - clear claims with evidence 4. Trust assets: - about page and editorial policy - contact and business identity - privacy and terms links 5. User value: - intent match - completeness - actionable depth ## Risk Flags Flag as high risk when you find: - anonymous or credential-free expert claims on high-stakes pages - copied or near-duplicate boilerplate across key landing pages - medical/financial/legal recommendations without sourcing - exaggerated claims without verifiable proof ## Scoring Output Format Return: 1. Overall content quality score (0-100) 2. Per-dimension scores mapped from rubric 3. Top 3 trust risks 4. Fastest high-impact remediation actions ## Remediation Patterns - Add expert-reviewed blocks with named reviewers on high-stakes pages. - Replace generic intros with first-hand evidence and data-backed claims. - Add citation discipline: primary sources first, then secondary context. - Standardize author bylines, update dates, and revision ownership.