Research Claim Map

Table of Contents

1. Purpose
2. When to Use
3. What Is It
4. Workflow
5. Evidence Quality Framework
6. Source Credibility Assessment
7. Common Patterns
8. Guardrails
9. Quick Reference

Purpose

Research Claim Map helps you systematically evaluate claims by triangulating sources, assessing evidence quality, identifying limitations, and reaching evidence-based conclusions. It prevents confirmation bias, overconfidence, and reliance on unreliable sources.

When to Use

Invoke this skill when you need to:

• Verify factual claims before making decisions or recommendations
• Evaluate conflicting evidence from multiple sources
• Assess vendor claims, product benchmarks, or competitive intelligence
• Conduct due diligence on business assertions (revenue, customers, capabilities)
• Fact-check news stories, social media claims, or viral statements
• Review academic literature for research validity
• Investigate potential misinformation or misleading statistics
• Rate evidence strength for policy decisions or strategic planning
• Triangulate eyewitness accounts or historical records
• Identify knowledge gaps and areas requiring further investigation

User phrases that trigger this skill:

• "Is this claim true?"
• "Can you verify this?"
• "Fact-check this statement"
• "I found conflicting information about..."
• "How reliable is this source?"
• "What's the evidence for..."
• "Due diligence on..."
• "Evaluate these competing claims"

What Is It

A Research Claim Map is a structured analysis that breaks down a claim into:

1. Claim statement (specific, testable assertion)
2. Evidence for (sources supporting the claim, rated by quality)
3. Evidence against (sources contradicting the claim, rated by quality)
4. Source credibility (expertise, bias, track record for each source)
5. Limitations (gaps, uncertainties, assumptions)
6. Conclusion (confidence level, decision recommendation)

Quick example:

• Claim: "Competitor X has 10,000 paying customers"
• Evidence for: Press release (secondary), case study count (tertiary)
• Evidence against: Industry analyst estimate of 3,000 (secondary)
• Credibility: Press release (biased source), analyst (independent but uncertain methodology)
• Limitations: No primary source verification, customer definition unclear
• Conclusion: Low confidence (40%) - likely inflated, need primary verification

Workflow

Copy this checklist and track your progress:

Research Claim Map Progress:
- [ ] Step 1: Define the claim precisely
- [ ] Step 2: Gather and categorize evidence
- [ ] Step 3: Rate evidence quality and source credibility
- [ ] Step 4: Identify limitations and gaps
- [ ] Step 5: Draw evidence-based conclusion

Step 1: Define the claim precisely

Restate the claim as a specific, testable assertion. Avoid vague language - use numbers, dates, and clear terms. See Common Patterns for claim reformulation examples.

Step 2: Gather and categorize evidence

Collect sources supporting and contradicting the claim. Organize into "Evidence For" and "Evidence Against". For straightforward verification → Use resources/template.md. For complex multi-source investigations → Study resources/methodology.md.

Step 3: Rate evidence quality and source credibility

Apply Evidence Quality Framework to rate each source (primary/secondary/tertiary). Apply Source Credibility Assessment to evaluate expertise, bias, and track record.

Step 4: Identify limitations and gaps

Document what's unknown, what assumptions were made, and where evidence is weak or missing. See resources/methodology.md for gap analysis techniques.

Step 5: Draw evidence-based conclusion

Synthesize findings into confidence level (0-100%) and actionable recommendation (believe/skeptical/reject claim). Self-check using resources/evaluators/rubric_research_claim_map.json before delivering. Minimum standard: Average score ≥ 3.5.

Evidence Quality Framework

Rating scale:

Primary Evidence (Strongest):

• Direct observation or measurement
• Original data or records
• First-hand accounts from participants
• Raw datasets, transaction logs
• Example: Sales database showing 10,000 customer IDs

Secondary Evidence (Medium):

• Analysis or interpretation of primary sources
• Expert synthesis of multiple primary sources
• Peer-reviewed research papers
• Verified news reporting with primary source citations
• Example: Industry analyst report analyzing public filings

Tertiary Evidence (Weakest):

• Summaries of secondary sources
• Textbooks, encyclopedias, Wikipedia
• Press releases, marketing materials
• Anecdotal reports without verification
• Example: Company blog post claiming customer count

Non-Evidence (Unreliable):

• Unverified social media posts
• Anonymous claims
• "Experts say" without attribution
• Circular references (A cites B, B cites A)
• Example: Viral tweet with no source

Source Credibility Assessment

Evaluate each source on:

Expertise (Does source have relevant knowledge?):

• High: Domain expert with credentials, track record
• Medium: Knowledgeable but not specialist
• Low: No demonstrated expertise

Independence (Is source biased or conflicted?):

• High: Independent, no financial/personal stake
• Medium: Some potential bias, disclosed
• Low: Direct financial interest, undisclosed conflicts

Track Record (Has source been accurate before?):

• High: Consistent accuracy, corrections when wrong
• Medium: Mixed record or unknown history
• Low: History of errors, retractions, unreliability

Methodology (How did source obtain information?):

• High: Transparent, replicable, rigorous
• Medium: Some methodology disclosed
• Low: Opaque, unverifiable, cherry-picked

Common Patterns

Pattern 1: Vendor Claim Verification

• Claim type: Product performance, customer count, ROI
• Approach: Seek independent verification (analysts, customers), test claims yourself
• Red flags: Only vendor sources, vague metrics, "up to X%" ranges

Pattern 2: Academic Literature Review

• Claim type: Research findings, causal claims
• Approach: Check for replication studies, meta-analyses, competing explanations
• Red flags: Single study, small sample, conflicts of interest, p-hacking

Pattern 3: News Fact-Checking

• Claim type: Events, statistics, quotes
• Approach: Trace to primary source, check multiple outlets, verify context
• Red flags: Anonymous sources, circular reporting, sensational framing

Pattern 4: Statistical Claims

• Claim type: Percentages, trends, correlations
• Approach: Check methodology, sample size, base rates, confidence intervals
• Red flags: Cherry-picked timeframes, denominator unclear, correlation ≠ causation

Guardrails

Avoid common biases:

• Confirmation bias: Actively seek evidence against your hypothesis
• Authority bias: Don't accept claims just because source is prestigious
• Recency bias: Older evidence can be more reliable than latest claims
• Availability bias: Vivid anecdotes ≠ representative data

Quality standards:

• Rate confidence numerically (0-100%), not vague terms ("probably", "likely")
• Document all assumptions explicitly
• Distinguish "no evidence found" from "evidence of absence"
• Update conclusions as new evidence emerges
• Flag when evidence quality is insufficient for confident conclusion

Ethical considerations:

• Respect source privacy and attribution
• Avoid cherry-picking evidence to support desired conclusion
• Acknowledge limitations and uncertainties
• Correct errors promptly when found

Quick Reference

Resources:

• Quick verification: resources/template.md
• Complex investigations: resources/methodology.md
• Quality rubric: resources/evaluators/rubric_research_claim_map.json

Evidence hierarchy: Primary > Secondary > Tertiary

Credibility factors: Expertise + Independence + Track Record + Methodology

Confidence calibration:

• 90-100%: Near certain, multiple primary sources, high credibility
• 70-89%: Confident, strong secondary sources, some limitations
• 50-69%: Uncertain, conflicting evidence or weak sources
• 30-49%: Skeptical, more evidence against than for
• 0-29%: Likely false, strong evidence against