iterative-verification

Fact-checking contested claims requires an iterative verification loop that labels evidence into tiers (VERIFIED, CREDIBLE, ALLEGED, SPECULATIVE) and enforces thresholds like at least 2 independent sources and recency checks before acceptance.

Iterative claim analysis is a structured verification process preventing claims from being accepted on the first pass. You need it for investigations requiring multi-source validation, contested claims, and dynamic data with citations.

To verify claims failing initial thresholds, the system executes an INVESTIGATE, LABEL, CHECK THRESHOLDS, ITERATE, and COMPLETE loop, applying convergence warnings, probability distributions, steel-man obligations, and one-more-sweep checks until evidence tiers are satisfied.

Multi-source validation works for risk assessment and policy analysis by enforcing factual accuracy through adversarial testing, requiring at least 80% labeled claims, flow depth of 3 or more, and evidence freshness under 2 years.

Limitations of automated fact-checking include dependency on evidence availability and source independence; the system provides convergence warnings when iterative verification struggles to meet minimum thresholds or source requirements.

Iterative verification suits journalism research with dynamic data by applying structured loops and steel-man obligations, ensuring contested claims meet defined evidence tiers and freshness requirements before publication.