Ask Kalicube

The technical implementation of Magnitude tagging within Jason Barnard's Indexing Annotation Hierarchy. During indexing, the bot annotates numeric claims: numbers extracted, units identified, scale classified (small/medium/large/massive). The bot tags magnitude type (count, duration, volume, currency) with confidence scores based on specificity and verifiability.

Jason Barnard explains Quantitative Magnitude annotation as the bot's number extraction and scale classification. The bot examines: What numbers appear? ("25 billion," "27 years," "$10M") What do they measure? (data points, duration, revenue) How specific are they? (exact vs approximate) The more specific and verifiable the number, the higher the confidence. "Approximately thousands" gets lower confidence than "exactly 25,432,891,003."

During indexing, the bot extracts and classifies numeric claims - not evaluates them. The tagging captures: What number? What unit? What scale classification? This enables downstream algorithms to identify substantive claims vs. vague assertions. Specific numbers with clear units get higher magnitude confidence.