Rough AI truth score
75/100
Sagan's practical norms—seek testable evidence, compare alternatives, quantify where possible, and distrust arguments from authority—remain excellent. Scientific literacy and critical-thinking tools help, but false beliefs are also driven by identity, trust, repetition, media systems, and social incentives, so a generic baloney-detection kit is not sufficient.
Based on three central claims · medium-high confidence
These are core norms of modern empirical inquiry and organized error correction. No checklist eliminates judgment or institutional failure, but converging methods, transparency, replication, and adversarial testing reduce dependence on authority and private intuition.
Knowledge, analytic reflection, and institutional trust correlate with resistance to some false claims, but relationships are domain-specific and politically mediated. Highly informed people can use reasoning defensively, while distrust may reflect real failures rather than simple ignorance.
Prebunking, lateral reading, and targeted media-literacy interventions can improve discernment, but transfer is incomplete and effects can decay. Identity, familiarity, source cues, emotion, platform design, and repeated exposure often overwhelm decontextualized critical-thinking lessons.
What this number means. It is an AI-generated first-pass judgment of three central factual or causal claims—not a rating, exhaustive fact-check, or human peer review. Claim credits are 100% for supported, 75% for mostly supported, 50% for mixed, and 25% for weak, then averaged and rounded. Lower confidence means the score should move more as better evidence arrives.
Method three-central-claims/0.1.0 · checked 2026-09-01 · 3/3 selected claims assessed · method and source audit