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UN Condemnation Statistics

UN Condemnation Statistics

by boxed·May 30, 2026·4 points·0 comments

AI Analysis

●●SolidBig BrainNiche Gem

LLM parsing 80 years of UN resolutions beats manual categorization.

Strengths
  • LLM extraction of condemnation clauses from decades of UN text is genuinely clever scale.
  • Multiple filter dimensions (UN body, wording strength, count type) enable real analysis.
  • Click-through to source resolutions provides transparency and verifiability.
Weaknesses
  • No methodology section on LLM accuracy, error rates, or false positive handling.
  • Niche audience limits broader developer appeal beyond data viz enthusiasts.
Category
Target Audience

Journalists, researchers, policy analysts, politically engaged citizens

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UN Data Explorer · Our World in Data

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