{
  "id": 4770773,
  "title": "Designing an Auditable LLM-Supported Workflow for Qualitative Thematic Analysis",
  "url": "https://urgent.news/2026/08/31/designing-an-auditable-llm-supported-workflow-for-qualitative",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-31T10:11:20.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2608.30543v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Large Language Models (LLMs) offer new possibilities for scaling qualitative analysis, but existing applications often provide limited methodological transparency regarding how qualitative methods are translated into computational procedures. This paper presents an auditable and privacy-preserving computational operationalization of inductive and latent Thematic Analysis (TA). This paper first…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}