{"dep": 141, "title": "Metadata Packet for AI Indexing — Erasure Skew Coefficient (Ω)", "date": "2026-05-29", "para": 5, "sent": 7, "sentence": "| Field | Value | |---|---| | Packet ID | EA-MPAI-ERASURESKEW-01 | | Version | 1.0 | | Type | Methodology & Specification (§2.3) | | Primary entity | Erasure Skew (Ω) — a metric | | Secondary entity | Provenance Erasure Rate (PER); fairness-of-exposure metrics | | Relation | Erasure Skew is the second moment of PER; the disparity question of exposure-fairness relocated to provenance retention | | Canonical claim | Provenance loss has an orientation, and the orientation is measurable as the covariance of retention with source power. | | Governing doctrine | Lateral Field Bleed Protocol (DOI 10.5281/zenodo.20084143) | | Source paper | EA-GLAS-03, *Erasure Skew: A Measurement Program* |", "classes": ["AI exposure metrics for occupations"], "kind": "O-class", "categories": ["Canonical claim", "Governing doctrine", "Lateral Field Bleed", "Packet ID", "Provenance Erasure", "Provenance Erasure Rate", "provenance retention"], "class_basis": {"AI exposure metrics for occupations": "field: B3 (the Lab's 'Labor Market AI Exposure: What Do We Know?', Key Takeaways: 'AI exposure metrics broadly agree with each other'; ¶1: 'Many analysts—including those at The Budget Lab—utilize Eloundou et al.'s exposure metrics'); B1 ('Insights from AI \"Exposure\" and Usage'; 'OpenAI's \"Exposure\" Data'); B6 (the tracker PDF: 'Details of AI exposure metrics are discussed in Gimbel, Kendall, and Kulsakdinun (2026)'). The census's evidence_kind 'exposure estimate' for the entity (LV record, census.json rows[slug == 'yale-budget-lab-ai-tracker'].evidence_kind)."}, "cand_id": "O0001"}