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The most methodologically rigorous, fully transparent AI job-exposure index.

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# Methodology Changelog

All notable changes to the scoring methodology. Every entry corresponds to an INDEX_VERSION (semver) in packages/scoring/src/version.ts.

# [1.0.0] — UNRELEASED (draft)

Initial methodology.

  • Task-based scoring on ONET 30.x task statements, importance-weighted aggregation to occupations (ONET-SOC 2019).
  • Five dimensions: automatability 0.35, feasibility 0.20, cost_ratio 0.15, barriers 0.20 (inverted), adoption_velocity 0.10.
  • Three sub-scores per occupation: exposure, substitution (headline composite), augmentation — augmentation rated separately per task, outside the composite.
  • Multi-model LLM rater panel (≥2 frontier models via RATER_MODELS); confidence bounds (score_low/score_high) derived from rater disagreement envelopes.
  • 5% human review sample; expert Delphi overrides replace LLM bands where triggered.
  • Grounding research: docs/research/01-…04-*.md (compiled 2026-08-05).