{
  "id": 11736,
  "url": "https://arxiv.org/abs/2607.15879",
  "title": "DECODEM: Data Extraction from Corporate Organizational Documents via Enhanced Methods",
  "summary": "arXiv:2607.15879v1 Announce Type: cross Abstract: Much empirical legal research depends on translating unstructured text into structured variables. In corporate governance research as elsewhere, this translation has traditionally relied on human coding of documents such as charters and bylaws, a process that is costly, difficult to scale, and often opaque. This paper introduces DECODEM, a set of benchmark datasets for evaluating the automated extraction of corporate governance variables from org",
  "authors": "Jens Frankenreiter",
  "category": "research",
  "topics": "regulation",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-20T04:00:00.000Z",
  "fetched_at": "2026-07-20T05:10:09.534Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/11736",
  "original_url": "https://arxiv.org/abs/2607.15879",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}