A relationship claim should show its evidence.
Grey Mirror makes relationship analysis inspectable when strong claims have supporting windows, counts, examples, confidence labels, and access controls that protect private message evidence.
What counts as evidence?
Evidence is any report support that connects a claim to the uploaded thread: message references, timeline windows, participant counts, metric deltas, parser diagnostics, and confidence labels. Evidence is not a mood, hunch, or unsupported summary.
What are the evidence levels?
Grey Mirror evidence can exist at multiple levels. The right level depends on access, privacy, and what the stored result payload actually contains.
Message-level evidence
Specific message references or excerpts when the report payload and access gating allow it.
Window-level evidence
A time range where a signal appears repeatedly, such as repair after conflict or a cooling response pattern.
Metric-level evidence
Counts, deltas, confidence, and participant splits behind a score or tile.
Report-level evidence
Cross-metric agreement that explains the trajectory rather than a single isolated signal.
Why would evidence be withheld or marked low confidence?
A responsible report should hold back when the evidence is not safe to show or not strong enough to support the claim.
- The user does not have access to full evidence.
- The payload predates a feature or does not include evidence references.
- The parser could not preserve enough timestamps or participant labels.
- The metric is based on too few messages or too few repeated examples.
- Showing raw evidence would expose sensitive private content in the wrong surface.
How should confidence labels work?
Confidence labels should explain reliability, not decorate the UI. A high-confidence label needs enough data and supporting evidence; a low-confidence label should tell the user what is missing.
How is evidence protected?
Evidence belongs inside gated report surfaces, not public pages. Public pages can explain evidence standards, but they should never expose a user upload, private report artifact, or full-only evidence link.
Evidence standards and authoritative references
Grey Mirror's evidence approach aligns with established principles from digital forensics, legal evidence standards, and information quality frameworks.
These references inform our evidence standards but do not constitute legal advice. Grey Mirror is a pattern analysis tool, not a legal or clinical diagnostic.
Digital forensics chain of custody
Our evidence preservation follows principles from <a href="https://www.nist.gov/itl/sed/nist-special-publication-800-86" target="_blank" rel="noopener noreferrer">NIST SP 800-86: Guide to Integrating Forensic Techniques into Incident Response</a>, focusing on maintaining data integrity and traceability from export to analysis.
Legal evidence relevance and reliability
Our confidence bands and evidence thresholds draw from <a href="https://www.law.cornell.edu/rules/fre/rule_401" target="_blank" rel="noopener noreferrer">Federal Rules of Evidence Rule 401</a> (relevance) and <a href="https://www.law.cornell.edu/rules/fre/rule_402" target="_blank" rel="noopener noreferrer">Rule 402</a> (admissibility), adapted for pattern analysis rather than legal proceedings.
Information quality principles
Our evidence quality checks align with information quality frameworks that emphasize accuracy, completeness, and traceability of data sources. We apply these principles to message exports and parser outputs.
Frequently Asked Questions
What is a why-this-score explanation?
It is a short explanation of which evidence and metric families drove a score, including confidence and evidence quality.
Can public samples show private report evidence?
No. Public samples explain the format without exposing user uploads, private report artifacts, or account-scoped evidence identifiers.
Is missing evidence the same as a bad relationship signal?
No. Missing evidence means the report cannot support a conclusion.
References and methodology
Related Grey Mirror guides
- Relationship text analyzer
- Methodology
- Public white paper
- Metrics library
- Evidence standards
- Privacy and deletion
- AI sycophancy vs measured analysis
- Interactive sample report
- Relationship text analysis glossary
- Long-term pattern analysis
- Love language in texting
- iMessage analysis
- WhatsApp chat analysis
- Instagram DM analysis
- ChatGPT vs Grey Mirror
- Screenshots vs full thread
- Repair attempts in texting
- Conflict escalation patterns
- Emotion word frequency
- Texting anxiety signs
- Friendship text analysis
- Telegram text analysis
- Improve text communication
- Apology insufficiency case study
- Full thread vs screenshot case study
- Couples text message analyzer
- Analyze chat history for patterns
- SMS and Android text analysis
- Pricing and free preview
- Private relationship text analyzer
- Best relationship text analyzer
- Best text message analyzers 2026
- Chat analyzer comparison
- Red flag text analyzer
- Situationship text analyzer
- Analyze relationship texts
- Relationship pattern analysis case study
- Criticism in texts case study
- Dismissiveness case study
- Emotional availability case study
- Emotional labor case study
- Emotional tone drift case study
- Talking about problems case study
- Mixed signals case study
- Post-conflict patterns case study
- Power dynamics case study
- Reading subtext case study
- Validation in texts case study
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