Epistemic Audit Dashboard
The Epistemic Audit Dashboard evaluates how well your content is structured for credibility, machine legibility, and inference resilience. This tool analyzes the epistemic integrity of your writing across five key dimensions, from citation lineage to semantic precision, and calculates a composite TrustScore from 0 to 10. You’ll receive a detailed report that shows how your work performs under machine synthesis and system-level scrutiny, so you can identify risks, strengthen signal quality, and make your insight more durable in an AI-mediated world.
Epistemic Audit Dashboard
Paste your full article or draft content below. This tool will evaluate your content across five trust-critical dimensions and generate an overall TrustScore.
🧠 Lineage & Citation: 6.5
- 2 sources found
- 1 broken URL
- No DOI present
🧑🏫 Authorial Visibility: 8.0
- Author name found
- No structured metadata
- ORCID missing
📐 Structural Hierarchy: 7.5
- 3 H2 headings found
- Long paragraph blocks
📎 Semantic Precision: 6.0
- 4 vague terms detected
- 1 ambiguous sentence flagged
🔍 Summarization Resilience: 8.0
- Passes compression test
- Key claims well-isolated
What Your TrustScore Represents
Your TrustScore is a composite rating that reflects how well your content is structured for credibility, traceability, and resilience in machine-mediated environments. Each piece of content is evaluated across five epistemic dimensions. Together, they provide a holistic measure of content integrity in the inference economy.
Lineage & Citation: Are claims supported by sources? Are references clear, persistent, and machine-readable?
Authorial Visibility: Is the author clearly named, credentialed, and structurally linked to the content?
Structural Hierarchy: Is the content well-organized with headings, labels, and modular blocks?
Semantic Precision: Are claims clearly framed and free of vague or ambiguous language?
Summarization Resilience: Can the content survive LLM compression and synthesis without distortion?
Score Interpretation
8.5–10: Inference-Optimized, Highly credible and structured for machine synthesis.
7.0–8.4: Trust-Aligned, Solid architecture with minor improvements recommended.
5.0–6.9: Moderate Integrity, Some visibility and verifiability gaps present.
Below 5.0: Needs Major Revision, High risk of distortion, misattribution, or invisibility in AI outputs.
