Factor Analysis on Scoring Dimensions

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• PC1 explains 82% of variance in factor analysis of 2224 data points across 6 scoring dimensionsfinding0.797
  Dimensions are not independent; composite score is the reliable signal; six dimensions useful for understanding how not how much
• Boundary Awareness (scoring dimension)concept0.789
  Scoring dimension weighted 0.10; measures navigating limits without collapse or pretense; sourced from Levin cognitive light cone and Buddhist non-self
• Importance Scoringmethod0.782
  Weighted Spearman correlation that corrects for sampling bias in automated interpretability evaluation
• Care Signal (scoring dimension)concept0.766
  Scoring dimension weighted 0.15; measures investment beyond task completion; sourced from SCI framework
• Aesthetic Response (scoring dimension)concept0.763
  Scoring dimension weighted 0.15; measures taste, discrimination, recognition of aliveness; sourced from Alexander's QWAN and SCI
• multiplicative scoringconcept0.747
  Score = (sum of completed quartet values) × (number of quartets), making portfolio composition consequential.
• Proper scoring ruleconcept0.746
  A scoring rule optimized by predicting true probabilities; log-loss is one.
• multiplicative scoring ruleconcept0.745
  Score = (sum of completed quartet values) × (number of completed quartets), rewarding breadth.