{
  "id": 3294,
  "url": "https://arxiv.org/abs/2606.01982v1",
  "title": "An NLP-Driven Framework for Curriculum-Labor Market Alignment: Schema-Constrained LLM Extraction, ESCO-Anchored Semantic Matching, and Multi-Dimensional Gap Quantification",
  "summary": "Schema-constrained information extraction from diverse educational and labor-market corpora remains an open challenge in natural language processing because existing pipelines rely primarily on lexical-surface methods that cannot recover implicit competencies, lack grounding in shared taxonomies, and provide no formal measures of extraction reliability or document-level completeness. To address these limitations, this paper proposes a four-stage NLP framework that combines (i) schema-constrained",
  "authors": "Sherzod Turaev, Mary John, Mamoun Awad, Nazar Zaki, Khaled Shuaib",
  "category": "research",
  "topics": "safety-alignment,jobs-economy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-01T09:44:37.000Z",
  "fetched_at": "2026-07-14T16:30:09.960Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3294",
  "original_url": "https://arxiv.org/abs/2606.01982v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}